# include <cmath>
# include <cstdlib>
# include <cstring>
# include <ctime>
# include <iomanip>
# include <iostream>

using namespace std;

# include "truncated_normal.hpp"

//****************************************************************************80

int i4_uniform_ab ( int a, int b, int &seed )

//****************************************************************************80
//
//  Purpose:
//
//    I4_UNIFORM_AB returns a scaled pseudorandom I4 between A and B.
//
//  Discussion:
//
//    The pseudorandom number should be uniformly distributed
//    between A and B.
//
//  Licensing:
//
//    This code is distributed under the MIT license. 
//
//  Modified:
//
//    02 October 2012
//
//  Author:
//
//    John Burkardt
//
//  Reference:
//
//    Paul Bratley, Bennett Fox, Linus Schrage,
//    A Guide to Simulation,
//    Second Edition,
//    Springer, 1987,
//    ISBN: 0387964673,
//    LC: QA76.9.C65.B73.
//
//    Bennett Fox,
//    Algorithm 647:
//    Implementation and Relative Efficiency of Quasirandom
//    Sequence Generators,
//    ACM Transactions on Mathematical Software,
//    Volume 12, Number 4, December 1986, pages 362-376.
//
//    Pierre L'Ecuyer,
//    Random Number Generation,
//    in Handbook of Simulation,
//    edited by Jerry Banks,
//    Wiley, 1998,
//    ISBN: 0471134031,
//    LC: T57.62.H37.
//
//    Peter Lewis, Allen Goodman, James Miller,
//    A Pseudo-Random Number Generator for the System/360,
//    IBM Systems Journal,
//    Volume 8, Number 2, 1969, pages 136-143.
//
//  Parameters:
//
//    Input, int A, B, the limits of the interval.
//
//    Input/output, int &SEED, the "seed" value, which should NOT be 0.
//    On output, SEED has been updated.
//
//    Output, int I4_UNIFORM_AB, a number between A and B.
//
{
  int c;
  const int i4_huge = 2147483647;
  int k;
  float r;
  int value;

  if ( seed == 0 )
  {
    cerr << "\n";
    cerr << "I4_UNIFORM_AB - Fatal error!\n";
    cerr << "  Input value of SEED = 0.\n";
    exit ( 1 );
  }
//
//  Guarantee A <= B.
//
  if ( b < a )
  {
    c = a;
    a = b;
    b = c;
  }

  k = seed / 127773;

  seed = 16807 * ( seed - k * 127773 ) - k * 2836;

  if ( seed < 0 )
  {
    seed = seed + i4_huge;
  }

  r = ( float ) ( seed ) * 4.656612875E-10;
//
//  Scale R to lie between A-0.5 and B+0.5.
//
  r = ( 1.0 - r ) * ( ( float ) a - 0.5 ) 
    +         r   * ( ( float ) b + 0.5 );
//
//  Use rounding to convert R to an integer between A and B.
//
  value = round ( r );
//
//  Guarantee A <= VALUE <= B.
//
  if ( value < a )
  {
    value = a;
  }
  if ( b < value )
  {
    value = b;
  }

  return value;
}
//****************************************************************************80

double normal_01_cdf ( double x )

//****************************************************************************80
//
//  Purpose:
//
//    NORMAL_01_CDF evaluates the Normal 01 CDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    10 February 1999
//
//  Author:
//
//    John Burkardt
//
//  Reference:
//
//    A G Adams,
//    Areas Under the Normal Curve,
//    Algorithm 39,
//    Computer j.,
//    Volume 12, pages 197-198, 1969.
//
//  Parameters:
//
//    Input, double X, the argument of the CDF.
//
//    Output, double CDF, the value of the CDF.
//
{
  double a1 = 0.398942280444;
  double a2 = 0.399903438504;
  double a3 = 5.75885480458;
  double a4 = 29.8213557808;
  double a5 = 2.62433121679;
  double a6 = 48.6959930692;
  double a7 = 5.92885724438;
  double b0 = 0.398942280385;
  double b1 = 3.8052E-08;
  double b2 = 1.00000615302;
  double b3 = 3.98064794E-04;
  double b4 = 1.98615381364;
  double b5 = 0.151679116635;
  double b6 = 5.29330324926;
  double b7 = 4.8385912808;
  double b8 = 15.1508972451;
  double b9 = 0.742380924027;
  double b10 = 30.789933034;
  double b11 = 3.99019417011;
  double cdf;
  double q;
  double y;
//
//  |X| <= 1.28.
//
  if ( fabs ( x ) <= 1.28 )
  {
    y = 0.5 * x * x;

    q = 0.5 - fabs ( x ) * ( a1 - a2 * y / ( y + a3 - a4 / ( y + a5
      + a6 / ( y + a7 ) ) ) );
//
//  1.28 < |X| <= 12.7
//
  }
  else if ( fabs ( x ) <= 12.7 )
  {
    y = 0.5 * x * x;

    q = exp ( - y ) * b0 / ( fabs ( x ) - b1
      + b2  / ( fabs ( x ) + b3
      + b4  / ( fabs ( x ) - b5
      + b6  / ( fabs ( x ) + b7
      - b8  / ( fabs ( x ) + b9
      + b10 / ( fabs ( x ) + b11 ) ) ) ) ) );
//
//  12.7 < |X|
//
  }
  else
  {
    q = 0.0;
  }
//
//  Take account of negative X.
//
  if ( x < 0.0 )
  {
    cdf = q;
  }
  else
  {
    cdf = 1.0 - q;
  }

  return cdf;
}
//****************************************************************************80

double normal_01_cdf_inv ( double p )

//****************************************************************************80
//
//  Purpose:
//
//    NORMAL_01_CDF_INV inverts the standard normal CDF.
//
//  Discussion:
//
//    The result is accurate to about 1 part in 10**16.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    27 December 2004
//
//  Author:
//
//    Original FORTRAN77 version by Michael Wichura.
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Michael Wichura,
//    The Percentage Points of the Normal Distribution,
//    Algorithm AS 241,
//    Applied Statistics,
//    Volume 37, Number 3, pages 477-484, 1988.
//
//  Parameters:
//
//    Input, double P, the value of the cumulative probability
//    densitity function.  0 < P < 1.  If P is outside this range, an
//    "infinite" value is returned.
//
//    Output, double NORMAL_01_CDF_INV, the normal deviate value
//    with the property that the probability of a standard normal deviate being
//    less than or equal to this value is P.
//
{
  double a[8] = {
    3.3871328727963666080,     1.3314166789178437745E+2,
    1.9715909503065514427E+3,  1.3731693765509461125E+4,
    4.5921953931549871457E+4,  6.7265770927008700853E+4,
    3.3430575583588128105E+4,  2.5090809287301226727E+3 };
  double b[8] = {
    1.0,                       4.2313330701600911252E+1,
    6.8718700749205790830E+2,  5.3941960214247511077E+3,
    2.1213794301586595867E+4,  3.9307895800092710610E+4,
    2.8729085735721942674E+4,  5.2264952788528545610E+3 };
  double c[8] = {
    1.42343711074968357734,     4.63033784615654529590,
    5.76949722146069140550,     3.64784832476320460504,
    1.27045825245236838258,     2.41780725177450611770E-1,
    2.27238449892691845833E-2,  7.74545014278341407640E-4 };
  double const1 = 0.180625;
  double const2 = 1.6;
  double d[8] = {
    1.0,                        2.05319162663775882187,
    1.67638483018380384940,     6.89767334985100004550E-1,
    1.48103976427480074590E-1,  1.51986665636164571966E-2,
    5.47593808499534494600E-4,  1.05075007164441684324E-9 };
  double e[8] = {
    6.65790464350110377720,     5.46378491116411436990,
    1.78482653991729133580,     2.96560571828504891230E-1,
    2.65321895265761230930E-2,  1.24266094738807843860E-3,
    2.71155556874348757815E-5,  2.01033439929228813265E-7 };
  double f[8] = {
    1.0,                        5.99832206555887937690E-1,
    1.36929880922735805310E-1,  1.48753612908506148525E-2,
    7.86869131145613259100E-4,  1.84631831751005468180E-5,
    1.42151175831644588870E-7,  2.04426310338993978564E-15 };
  double q;
  double r;
  double split1 = 0.425;
  double split2 = 5.0;
  double value;

  if ( p <= 0.0 )
  {
    value = - HUGE_VAL;
    return value;
  }

  if ( 1.0 <= p )
  {
    value = HUGE_VAL;
    return value;
  }

  q = p - 0.5;

  if ( fabs ( q ) <= split1 )
  {
    r = const1 - q * q;
    value = q * r8poly_value_horner ( 7, a, r ) 
              / r8poly_value_horner ( 7, b, r );
  }
  else
  {
    if ( q < 0.0 )
    {
      r = p;
    }
    else
    {
      r = 1.0 - p;
    }

    if ( r <= 0.0 )
    {
      value = HUGE_VAL;
    }
    else
    {
      r = sqrt ( - log ( r ) );

      if ( r <= split2 )
      {
        r = r - const2;
        value = r8poly_value_horner ( 7, c, r ) 
              / r8poly_value_horner ( 7, d, r );
       }
       else
       {
         r = r - split2;
         value = r8poly_value_horner ( 7, e, r ) 
               / r8poly_value_horner ( 7, f, r );
      }
    }

    if ( q < 0.0 )
    {
      value = - value;
    }

  }

  return value;
}
//****************************************************************************80

void normal_01_cdf_values ( int &n_data, double &x, double &fx )

//****************************************************************************80
//
//  Purpose:
//
//    NORMAL_01_CDF_VALUES returns some values of the Normal 01 CDF.
//
//  Discussion:
//
//    In Mathematica, the function can be evaluated by:
//
//      Needs["Statistics`ContinuousDistributions`"]
//      dist = NormalDistribution [ 0, 1 ]
//      CDF [ dist, x ]
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    28 August 2004
//
//  Author:
//
//    John Burkardt
//
//  Reference:
//
//    Milton Abramowitz, Irene Stegun,
//    Handbook of Mathematical Functions,
//    National Bureau of Standards, 1964,
//    ISBN: 0-486-61272-4,
//    LC: QA47.A34.
//
//    Stephen Wolfram,
//    The Mathematica Book,
//    Fourth Edition,
//    Cambridge University Press, 1999,
//    ISBN: 0-521-64314-7,
//    LC: QA76.95.W65.
//
//  Parameters:
//
//    Input/output, int &N_DATA.  The user sets N_DATA to 0 before the
//    first call.  On each call, the routine increments N_DATA by 1, and
//    returns the corresponding data; when there is no more data, the
//    output value of N_DATA will be 0 again.
//
//    Output, double &X, the argument of the function.
//
//    Output, double &FX, the value of the function.
//
{
# define N_MAX 17

  static double fx_vec[N_MAX] = {
     0.5000000000000000E+00,
     0.5398278372770290E+00,
     0.5792597094391030E+00,
     0.6179114221889526E+00,
     0.6554217416103242E+00,
     0.6914624612740131E+00,
     0.7257468822499270E+00,
     0.7580363477769270E+00,
     0.7881446014166033E+00,
     0.8159398746532405E+00,
     0.8413447460685429E+00,
     0.9331927987311419E+00,
     0.9772498680518208E+00,
     0.9937903346742239E+00,
     0.9986501019683699E+00,
     0.9997673709209645E+00,
     0.9999683287581669E+00 };

  static double x_vec[N_MAX] = {
     0.0000000000000000E+00,
     0.1000000000000000E+00,
     0.2000000000000000E+00,
     0.3000000000000000E+00,
     0.4000000000000000E+00,
     0.5000000000000000E+00,
     0.6000000000000000E+00,
     0.7000000000000000E+00,
     0.8000000000000000E+00,
     0.9000000000000000E+00,
     0.1000000000000000E+01,
     0.1500000000000000E+01,
     0.2000000000000000E+01,
     0.2500000000000000E+01,
     0.3000000000000000E+01,
     0.3500000000000000E+01,
     0.4000000000000000E+01 };

  if ( n_data < 0 )
  {
    n_data = 0;
  }

  n_data = n_data + 1;

  if ( N_MAX < n_data )
  {
    n_data = 0;
    x = 0.0;
    fx = 0.0;
  }
  else
  {
    x = x_vec[n_data-1];
    fx = fx_vec[n_data-1];
  }

  return;
# undef N_MAX
}
//****************************************************************************80

double normal_01_mean ( )

//****************************************************************************80
//
//  Purpose:
//
//    NORMAL_01_MEAN returns the mean of the Normal 01 PDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    18 September 2004
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Output, double MEAN, the mean of the PDF.
//
{
  double mean;

  mean = 0.0;

  return mean;
}
//****************************************************************************80

double normal_01_moment ( int order )

//****************************************************************************80
//
//  Purpose:
//
//    NORMAL_01_MOMENT evaluates moments of the Normal 01 PDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    31 August 2013
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, int ORDER, the order of the moment.
//    0 <= ORDER.
//
//    Output, double NORMAL_01_MOMENT, the value of the moment.
//
{
  double value;

  if ( ( order % 2 ) == 0 )
  {
    value = r8_factorial2 ( order - 1 );
  }
  else
  {
    value = 0.0;
  }

  return value;
}
//****************************************************************************80

double normal_01_pdf ( double x )

//****************************************************************************80
//
//  Purpose:
//
//    NORMAL_01_PDF evaluates the Normal 01 PDF.
//
//  Discussion:
//
//    The Normal 01 PDF is also called the "Standard Normal" PDF, or
//    the Normal PDF with 0 mean and standard deviation 1.
//
//    PDF(X) = exp ( - 0.5 * X^2 ) / sqrt ( 2 * PI )
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    18 September 2004
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, double X, the argument of the PDF.
//
//    Output, double PDF, the value of the PDF.
//
{
  double pdf;
  const double r8_pi = 3.14159265358979323;

  pdf = exp ( -0.5 * x * x ) / sqrt ( 2.0 * r8_pi );

  return pdf;
}
//****************************************************************************80

double normal_01_sample ( int &seed )

//****************************************************************************80
//
//  Purpose:
//
//    NORMAL_01_SAMPLE samples the standard normal probability distribution.
//
//  Discussion:
//
//    The standard normal probability distribution function (PDF) has
//    mean 0 and standard deviation 1.
//
//    The Box-Muller method is used, which is efficient, but
//    generates two values at a time.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    26 August 2013
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input/output, int &SEED, a seed for the random number generator.
//
//    Output, double NORMAL_01_SAMPLE, a normally distributed random value.
//
{
  double r1;
  double r2;
  const double r8_pi = 3.14159265358979323;
  double x;

  r1 = r8_uniform_01 ( seed );
  r2 = r8_uniform_01 ( seed );
  x = sqrt ( -2.0 * log ( r1 ) ) * cos ( 2.0 * r8_pi * r2 );

  return x;
}
//****************************************************************************80

double normal_01_variance ( )

//****************************************************************************80
//
//  Purpose:
//
//    NORMAL_01_VARIANCE returns the variance of the Normal 01 PDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    18 September 2004
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Output, double VARIANCE, the variance of the PDF.
//
{
  double variance;

  variance = 1.0;

  return variance;
}
//****************************************************************************80

double normal_ms_cdf ( double x, double mu, double sigma )

//****************************************************************************80
//
//  Purpose:
//
//    NORMAL_MS_CDF evaluates the Normal CDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    19 September 2004
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, double X, the argument of the CDF.
//
//    Input, double MU, SIGMA, the parameters of the PDF.
//    0.0 < SIGMA.
//
//    Output, double NORMAL_MS_CDF, the value of the CDF.
//
{
  double cdf;
  double y;

  y = ( x - mu ) / sigma;

  cdf = normal_01_cdf ( y );

  return cdf;
}
//****************************************************************************80

double normal_ms_cdf_inv ( double cdf, double mu, double sigma )

//****************************************************************************80
//
//  Purpose:
//
//    NORMAL_MS_CDF_INV inverts the Normal CDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    19 September 2004
//
//  Author:
//
//    John Burkardt
//
//  Reference:
//
//    Algorithm AS 111,
//    Applied Statistics,
//    Volume 26, pages 118-121, 1977.
//
//  Parameters:
//
//    Input, double CDF, the value of the CDF.
//    0.0 <= CDF <= 1.0.
//
//    Input, double MU, SIGMA, the parameters of the PDF.
//    0.0 < SIGMA.
//
//    Output, double NORMAL_MS_CDF_INV, the corresponding argument.
//
{
  double x;
  double x2;

  if ( cdf < 0.0 || 1.0 < cdf )
  {
    cout << "\n";
    cout << "NORMAL_MS_CDF_INV - Fatal error!\n";
    cout << "  CDF < 0 or 1 < CDF.\n";
    exit ( 1 );
  }

  x2 = normal_01_cdf_inv ( cdf );

  x = mu + sigma * x2;

  return x;
}
//****************************************************************************80

double normal_ms_mean ( double mu, double sigma )

//****************************************************************************80
//
//  Purpose:
//
//    NORMAL_MS_MEAN returns the mean of the Normal PDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    19 September 2004
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, double MU, SIGMA, the parameters of the PDF.
//    0.0 < SIGMA.
//
//    Output, double NORMAL_MS_MEAN, the mean of the PDF.
//
{
  double mean;

  mean = mu;

  return mean;
}
//****************************************************************************80

double normal_ms_moment ( int order, double mu, double sigma )

//****************************************************************************80
//
//  Purpose:
//
//    NORMAL_MS_MOMENT evaluates moments of the Normal PDF.
//
//  Discussion:
//
//    The formula was posted by John D Cook.
//
//    Order  Moment
//    -----  ------
//      0    1
//      1    mu
//      2    mu^2 +         sigma^2
//      3    mu^3 +  3 mu   sigma^2
//      4    mu^4 +  6 mu^2 sigma^2 +   3      sigma^4
//      5    mu^5 + 10 mu^3 sigma^2 +  15 mu   sigma^4
//      6    mu^6 + 15 mu^4 sigma^2 +  45 mu^2 sigma^4 +  15      sigma^6
//      7    mu^7 + 21 mu^5 sigma^2 + 105 mu^3 sigma^4 + 105 mu   sigma^6
//      8    mu^8 + 28 mu^6 sigma^2 + 210 mu^4 sigma^4 + 420 mu^2 sigma^6 
//           + 105 sigma^8
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    31 August 2013
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, int ORDER, the order of the moment.
//    0 <= ORDER.
//
//    Input, double MU, the mean of the distribution.
//
//    Input, double SIGMA, the standard deviation of the distribution.
//
//    Output, double NORMAL_MS_MOMENT, the value of the central moment.
//
{
  int j;
  int j_hi;
  double value;

  j_hi = ( order / 2 );

  value = 0.0; 
  for ( j = 0; j <= j_hi; j++ )
  {
    value = value 
      + r8_choose ( order, 2 * j ) 
      * r8_factorial2 ( 2 * j - 1 ) 
      * pow ( mu, order - 2 * j ) * pow ( sigma, 2 * j );
  }

  return value;
}
//****************************************************************************80

double normal_ms_moment_central ( int order, double mu, double sigma )

//****************************************************************************80
//
//  Purpose:
//
//    NORMAL_MS_MOMENT_CENTRAL evaluates central moments of the Normal PDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    31 August 2013
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, int ORDER, the order of the moment.
//    0 <= ORDER.
//
//    Input, double MU, the mean of the distribution.
//
//    Input, double SIGMA, the standard deviation of the distribution.
//
//    Output, double NORMAL_MS_MOMENT_CENTRAL, the value of the central moment.
//
{
  double value;

  if ( ( order % 2 ) == 0 )
  {
    value = r8_factorial2 ( order - 1 ) * pow ( sigma, order );
  }
  else
  {
    value = 0.0;  
  }

  return value;
}
//****************************************************************************80

double normal_ms_moment_central_values ( int order, double mu, double sigma )

//****************************************************************************80
//
//  Purpose:
//
//    NORMAL_MS_MOMENT_CENTRAL_VALUES: moments 0 through 10 of the Normal PDF.
//
//  Discussion:
//
//    The formula was posted by John D Cook.
//
//    Order  Moment
//    -----  ------
//      0    1
//      1    0
//      2    sigma^2
//      3    0
//      4    3 sigma^4
//      5    0
//      6    15 sigma^6
//      7    0
//      8    105 sigma^8
//      9    0
//     10    945 sigma^10
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    31 August 2013
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, int ORDER, the order of the moment.
//    0 <= ORDER <= 10.
//
//    Input, double MU, the mean of the distribution.
//
//    Input, double SIGMA, the standard deviation of the distribution.
//
//    Output, double NORMAL_MS_MOMENT_CENTRAL_VALUES, the value of the 
//    central moment.
//
{
  double value;

  if ( order == 0 )
  {
    value = 1.0;
  }
  else if ( order == 1 )
  {
    value = 0.0;
  }
  else if ( order == 2 )
  {
    value = pow ( sigma, 2 );
  }
  else if ( order == 3 )
  {
    value = 0.0;
  }
  else if ( order == 4 )
  {
    value = 3.0 * pow ( sigma, 4 );
  }
  else if ( order == 5 )
  {
    value = 0.0;
  }
  else if ( order == 6 )
  {
    value = 15.0 * pow ( sigma, 6 );
  }
  else if ( order == 7 )
  {
    value = 0.0;
  }
  else if ( order == 8 )
  {
    value = 105.0 * pow ( sigma, 8 );
  }
  else if ( order == 9 )
  {
    value = 0.0;
  }
  else if ( order == 10 )
  {
    value = 945.0 * pow ( sigma, 10 );
  }
  else
  {
    cerr << "\n";
    cerr << "NORMAL_MS_MOMENT_CENTRAL_VALUES - Fatal error!\n";
    cerr << "  Only ORDERS 0 through 10 are available.\n";
    exit ( 1 );
  }

  return value;
}
//****************************************************************************80

double normal_ms_moment_values ( int order, double mu, double sigma )

//****************************************************************************80
//
//  Purpose:
//
//    NORMAL_MS_MOMENT_VALUES evaluates moments 0 through 8 of the Normal PDF.
//
//  Discussion:
//
//    The formula was posted by John D Cook.
//
//    Order  Moment
//    -----  ------
//      0    1
//      1    mu
//      2    mu^2 +         sigma^2
//      3    mu^3 +  3 mu   sigma^2
//      4    mu^4 +  6 mu^2 sigma^2 +   3      sigma^4
//      5    mu^5 + 10 mu^3 sigma^2 +  15 mu   sigma^4
//      6    mu^6 + 15 mu^4 sigma^2 +  45 mu^2 sigma^4 +  15      sigma^6
//      7    mu^7 + 21 mu^5 sigma^2 + 105 mu^3 sigma^4 + 105 mu   sigma^6
//      8    mu^8 + 28 mu^6 sigma^2 + 210 mu^4 sigma^4 + 420 mu^2 sigma^6 
//           + 105 sigma^8
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    31 August 2013
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, int ORDER, the order of the moment.
//    0 <= ORDER <= 8.
//
//    Input, double MU, the mean of the distribution.
//
//    Input, double SIGMA, the standard deviation of the distribution.
//
//    Output, double NORMAL_MS_MOMENT_VALUES, the value of the central moment.
//
{
  double value;

  if ( order == 0 )
  {
    value = 1.0;
  }
  else if ( order == 1 )
  {
    value = mu;
  }
  else if ( order == 2 )
  {
    value = pow ( mu, 2 ) + pow ( sigma, 2 );
  }
  else if ( order == 3 )
  {
    value = pow ( mu, 3 ) + 3.0 * mu * pow ( sigma, 2 );
  }
  else if ( order == 4 )
  {
    value = pow ( mu, 4 ) + 6.0 * pow ( mu, 2 ) * pow ( sigma, 2 ) 
      + 3.0 * pow ( sigma, 4 );
  }
  else if ( order == 5 )
  {
    value = pow ( mu, 5 ) + 10.0 * pow ( mu, 3 ) * pow ( sigma, 2 ) 
      + 15.0 * mu * pow ( sigma, 4 );
  }
  else if ( order == 6 )
  {
    value = pow ( mu, 6 ) + 15.0 * pow ( mu, 4 ) * pow ( sigma, 2 ) 
      + 45.0 * pow ( mu, 2 ) * pow ( sigma, 4 ) 
      + 15.0 * pow ( sigma, 6 );
  }
  else if ( order == 7 )
  {
    value = pow ( mu, 7 ) + 21.0 * pow ( mu, 5 ) * pow ( sigma, 2 ) 
      + 105.0 * pow ( mu, 3 ) * pow ( sigma, 4 ) 
      + 105.0 * mu * pow ( sigma, 6 );
  }
  else if ( order == 8 )
  {
    value = pow ( mu, 8 ) + 28.0 * pow ( mu, 6 ) * pow ( sigma, 2 ) 
      + 210.0 * pow ( mu, 4 ) * pow ( sigma, 4 ) 
      + 420.0 * pow ( mu, 2 ) * pow ( sigma, 6 ) + 105.0 * pow ( sigma, 8 );
  }
  else
  {
    cerr << "\n";
    cerr << "NORMAL_MS_MOMENT_VALUES - Fatal error!\n";
    cerr << "  Only ORDERS 0 through 8 are available.\n";
    exit ( 1 );
  }

  return value;
}
//****************************************************************************80

double normal_ms_pdf ( double x, double mu, double sigma )

//****************************************************************************80
//
//  Purpose:
//
//    NORMAL_MS_PDF evaluates the Normal PDF.
//
//  Discussion:
//
//    PDF(MU,SIGMA;X)
//      = exp ( - 0.5 * ( ( X - MU ) / SIGMA )^2 )
//      / ( SIGMA * SQRT ( 2 * PI ) )
//
//    The normal PDF is also known as the Gaussian PDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    19 September 2004
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, double X, the argument of the PDF.
//
//    Input, double MU, SIGMA, the parameters of the PDF.
//    0.0 < SIGMA.
//
//    Output, double NORMAL_MS_PDF, the value of the PDF.
//
{
  double pdf;
  const double r8_pi = 3.14159265358979323;
  double y;

  y = ( x - mu ) / sigma;

  pdf = exp ( - 0.5 * y * y )  / ( sigma * sqrt ( 2.0 * r8_pi ) );

  return pdf;
}
//****************************************************************************80

double normal_ms_sample ( double mu, double sigma, int &seed )

//****************************************************************************80
//
//  Purpose:
//
//    NORMAL_MS_SAMPLE samples the Normal PDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    20 November 2004
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, double MU, SIGMA, the parameters of the PDF.
//    0.0 < SIGMA.
//
//    Input/output, int &SEED, a seed for the random number generator.
//
//    Output, double NORMAL_MS_SAMPLE, a sample of the PDF.
//
{
  double x;

  x = normal_01_sample ( seed );

  x = mu + sigma * x;

  return x;
}
//****************************************************************************80

double normal_ms_variance ( double mu, double sigma )

//****************************************************************************80
//
//  Purpose:
//
//    NORMAL_MS_VARIANCE returns the variance of the Normal PDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    19 September 2004
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, double MU, SIGMA, the parameters of the PDF.
//    0.0 < SIGMA.
//
//    Output, double NORMAL_MS_VARIANCE, the variance of the PDF.
//
{
  double variance;

  variance = sigma * sigma;

  return variance;
}
//****************************************************************************80

double r8_choose ( int n, int k )

//****************************************************************************80
//
//  Purpose:
//
//    R8_CHOOSE computes the binomial coefficient C(N,K) as an R8.
//
//  Discussion:
//
//    The value is calculated in such a way as to avoid overflow and
//    roundoff.  The calculation is done in R8 arithmetic.
//
//    The formula used is:
//
//      C(N,K) = N! / ( K! * (N-K)! )
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    09 June 2013
//
//  Author:
//
//    John Burkardt
//
//  Reference:
//
//    ML Wolfson, HV Wright,
//    Algorithm 160:
//    Combinatorial of M Things Taken N at a Time,
//    Communications of the ACM,
//    Volume 6, Number 4, April 1963, page 161.
//
//  Parameters:
//
//    Input, int N, K, the values of N and K.
//
//    Output, double R8_CHOOSE, the number of combinations of N
//    things taken K at a time.
//
{
  int i;
  int mn;
  int mx;
  double value;

  if ( k < n - k )
  {
    mn = k;
    mx = n - k;
  }
  else
  {
    mn = n - k;
    mx = k;
  }

  if ( mn < 0 )
  {
    value = 0.0;
  }
  else if ( mn == 0 )
  {
    value = 1.0;
  }
  else
  {
    value = ( double ) ( mx + 1 );

    for ( i = 2; i <= mn; i++ )
    {
      value = ( value * ( double ) ( mx + i ) ) / ( double ) i;
    }
  }

  return value;
}
//****************************************************************************80

double r8_factorial2 ( int n )

//****************************************************************************80
//
//  Purpose:
//
//    R8_FACTORIAL2 computes the double factorial function.
//
//  Discussion:
//
//    FACTORIAL2( N ) = Product ( N * (N-2) * (N-4) * ... * 2 )  (N even)
//                    = Product ( N * (N-2) * (N-4) * ... * 1 )  (N odd)
//
//  Example:
//
//     N Value
//
//     0     1
//     1     1
//     2     2
//     3     3
//     4     8
//     5    15
//     6    48
//     7   105
//     8   384
//     9   945
//    10  3840
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    22 January 2008
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, int N, the argument of the double factorial
//    function.  If N is less than 1, R8_FACTORIAL2 is returned as 1.0.
//
//    Output, double R8_FACTORIAL2, the value of Factorial2(N).
//
{
  int n_copy;
  double value;

  value = 1.0;

  if ( n < 1 )
  {
    return value;
  }

  n_copy = n;

  while ( 1 < n_copy )
  {
    value = value * ( double ) n_copy;
    n_copy = n_copy - 2;
  }

  return value;
}
//****************************************************************************80

void r8_factorial2_values ( int &n_data, int &n, double &f )

//****************************************************************************80
//
//  Purpose:
//
//    R8_FACTORIAL2_VALUES returns values of the double factorial function.
//
//  Formula:
//
//    FACTORIAL2( N ) = Product ( N * (N-2) * (N-4) * ... * 2 )  (N even)
//                    = Product ( N * (N-2) * (N-4) * ... * 1 )  (N odd)
//
//    In Mathematica, the function can be evaluated by:
//
//      n!!
//
//  Example:
//
//     N    N!!
//
//     0     1
//     1     1
//     2     2
//     3     3
//     4     8
//     5    15
//     6    48
//     7   105
//     8   384
//     9   945
//    10  3840
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    07 February 2015
//
//  Author:
//
//    John Burkardt
//
//  Reference:
//
//    Milton Abramowitz, Irene Stegun,
//    Handbook of Mathematical Functions,
//    National Bureau of Standards, 1964,
//    ISBN: 0-486-61272-4,
//    LC: QA47.A34.
//
//    Stephen Wolfram,
//    The Mathematica Book,
//    Fourth Edition,
//    Cambridge University Press, 1999,
//    ISBN: 0-521-64314-7,
//    LC: QA76.95.W65.
//
//    Daniel Zwillinger,
//    CRC Standard Mathematical Tables and Formulae,
//    30th Edition,
//    CRC Press, 1996, page 16.
//
//  Parameters:
//
//    Input/output, int &N_DATA.  The user sets N_DATA to 0 before the
//    first call.  On each call, the routine increments N_DATA by 1, and
//    returns the corresponding data; when there is no more data, the
//    output value of N_DATA will be 0 again.
//
//    Output, int &N, the argument of the function.
//
//    Output, double &F, the value of the function.
//
{
# define N_MAX 16

  static double f_vec[N_MAX] = {
          1.0,
          1.0,
          2.0,
          3.0,
          8.0,
         15.0,
         48.0,
        105.0,
        384.0,
        945.0,
       3840.0,
      10395.0,
      46080.0,
     135135.0,
     645120.0,
    2027025.0 };

  static int n_vec[N_MAX] = {
     0,
     1,  2,  3,  4,  5,
     6,  7,  8,  9, 10,
    11, 12, 13, 14, 15 };

  if ( n_data < 0 )
  {
    n_data = 0;
  }

  n_data = n_data + 1;

  if ( N_MAX < n_data )
  {
    n_data = 0;
    n = 0;
    f = 0.0;
  }
  else
  {
    n = n_vec[n_data-1];
    f = f_vec[n_data-1];
  }

  return;
# undef N_MAX
}
//****************************************************************************80

double r8_log_2 ( double x )

//****************************************************************************80
//
//  Purpose:
//
//    R8_LOG_2 returns the logarithm base 2 of the absolute value of an R8.
//
//  Discussion:
//
//    value = Log ( |X| ) / Log ( 2.0 )
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    22 March 2004
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, double X, the number whose base 2 logarithm is desired.
//    X should not be 0.
//
//    Output, double R8_LOG_2, the logarithm base 2 of the absolute
//    value of X.  It should be true that |X| = 2^R_LOG_2.
//
{
  double value;

  if ( x == 0.0 )
  {
    value = - HUGE_VAL;
  }
  else
  {
    value = log ( fabs ( x ) ) / log ( 2.0 );
  }

  return value;
}
//****************************************************************************80

double r8_mop ( int i )

//****************************************************************************80
//
//  Purpose:
//
//    R8_MOP returns the I-th power of -1 as an R8 value.
//
//  Discussion:
//
//    An R8 is an double value.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    16 November 2007
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, int I, the power of -1.
//
//    Output, double R8_MOP, the I-th power of -1.
//
{
  double value;

  if ( ( i % 2 ) == 0 )
  {
    value = 1.0;
  }
  else
  {
    value = -1.0;
  }

  return value;
}
//****************************************************************************80

double r8_uniform_01 ( int &seed )

//****************************************************************************80
//
//  Purpose:
//
//    R8_UNIFORM_01 returns a unit pseudorandom R8.
//
//  Discussion:
//
//    This routine implements the recursion
//
//      seed = 16807 * seed mod ( 2^31 - 1 )
//      unif = seed / ( 2^31 - 1 )
//
//    The integer arithmetic never requires more than 32 bits,
//    including a sign bit.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    11 August 2004
//
//  Author:
//
//    John Burkardt
//
//  Reference:
//
//    Paul Bratley, Bennett Fox, Linus Schrage,
//    A Guide to Simulation,
//    Springer Verlag, pages 201-202, 1983.
//
//    Bennett Fox,
//    Algorithm 647:
//    Implementation and Relative Efficiency of Quasirandom
//    Sequence Generators,
//    ACM Transactions on Mathematical Software,
//    Volume 12, Number 4, pages 362-376, 1986.
//
//  Parameters:
//
//    Input/output, int &SEED, the "seed" value.  Normally, this
//    value should not be 0.  On output, SEED has been updated.
//
//    Output, double R8_UNIFORM_01, a new pseudorandom variate, strictly between
//    0 and 1.
//
{
  int k;
  double r;

  k = seed / 127773;

  seed = 16807 * ( seed - k * 127773 ) - k * 2836;

  if ( seed < 0 )
  {
    seed = seed + 2147483647;
  }

  r = ( double ) ( seed ) * 4.656612875E-10;

  return r;
}
//****************************************************************************80

void r8poly_print ( int n, double a[], string title )

//****************************************************************************80
//
//  Purpose:
//
//    R8POLY_PRINT prints out a polynomial.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    26 February 2015
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, int N, the dimension of A.
//
//    Input, double A[N+1], the polynomial coefficients.
//    A(0) is the constant term and
//    A(N) is the coefficient of X^N.
//
//    Input, string TITLE, a title.
//
{
  int i;
  double mag;
  char plus_minus;

  cout << "\n";
  cout << title << "\n";
  cout << "\n";

  if ( n <= 0 )
  {
    cout << "  p(x) = 0\n";
    return;
  }

  if ( a[n] < 0.0 )
  {
    plus_minus = '-';
  }
  else
  {
    plus_minus = ' ';
  }

  mag = fabs ( a[n] );

  if ( 2 <= n )
  {
    cout << "  p(x) = " << plus_minus
         << setw(14) << mag << " * x ^ " << n << "\n";
  }
  else if ( n == 1 )
  {
    cout << "  p(x) = " << plus_minus
         << setw(14) << mag << " * x\n";
  }
  else if ( n == 0 )
  {
    cout << "  p(x) = " << plus_minus
         << setw(14) << mag << "\n";
  }

  for ( i = n - 1; 0 <= i; i-- )
  {
    if ( a[i] < 0.0 )
    {
      plus_minus = '-';
    }
    else
    {
      plus_minus = '+';
    }

    mag = fabs ( a[i] );

    if ( mag != 0.0 )
    {
      if ( 2 <= i )
      {
        cout << "         " << plus_minus
             << setw(14) << mag << " * x ^ " << i << "\n";
      }
      else if ( i == 1 )
      {
        cout << "         " << plus_minus
             << setw(14) << mag << " * x\n";
      }
      else if ( i == 0 )
      {
        cout << "         " << plus_minus
             << setw(14) << mag << "\n";
      }
    }
  }

  return;
}
//****************************************************************************80

double r8poly_value_horner ( int m, double c[], double x )

//****************************************************************************80
//
//  Purpose:
//
//    R8POLY_VALUE_HORNER evaluates a polynomial using Horner's method.
//
//  Discussion:
//
//    The polynomial 
//
//      p(x) = c0 + c1 * x + c2 * x^2 + ... + cm * x^m
//
//    is to be evaluated at the value X.
//
//  Licensing:
//
//    This code is distributed under the MIT license. 
//
//  Modified:
//
//    02 January 2015
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, int M, the degree of the polynomial.
//
//    Input, double C[M+1], the coefficients of the polynomial.
//    A[0] is the constant term.
//
//    Input, double X, the point at which the polynomial is to be evaluated.
//
//    Output, double R8POLY_VALUE_HORNER, the value of the polynomial at X.
//
{
  int i;
  double value;

  value = c[m];

  for ( i = m - 1; 0 <= i; i-- )
  {
    value = value * x + c[i];
  }

  return value;
}
//****************************************************************************80

double *r8vec_linspace_new ( int n, double a_first, double a_last )

//****************************************************************************80
//
//  Purpose:
//
//    R8VEC_LINSPACE_NEW creates a vector of linearly spaced values.
//
//  Discussion:
//
//    An R8VEC is a vector of R8's.
//
//    4 points evenly spaced between 0 and 12 will yield 0, 4, 8, 12.
//
//    In other words, the interval is divided into N-1 even subintervals,
//    and the endpoints of intervals are used as the points.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    29 March 2011
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, int N, the number of entries in the vector.
//
//    Input, double A_FIRST, A_LAST, the first and last entries.
//
//    Output, double R8VEC_LINSPACE_NEW[N], a vector of linearly spaced data.
//
{
  double *a;
  int i;

  a = new double[n];

  if ( n == 1 )
  {
    a[0] = ( a_first + a_last ) / 2.0;
  }
  else
  {
    for ( i = 0; i < n; i++ )
    {
      a[i] = ( ( double ) ( n - 1 - i ) * a_first 
             + ( double ) (         i ) * a_last ) 
             / ( double ) ( n - 1     );
    }
  }
  return a;
}
//****************************************************************************80

double r8vec_max ( int n, double *dvec )

//****************************************************************************80
//
//  Purpose:
//
//    R8VEC_MAX returns the value of the maximum element in an R8VEC.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    15 October 1998
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, int N, the number of entries in the array.
//
//    Input, double *RVEC, a pointer to the first entry of the array.
//
//    Output, double R8VEC_MAX, the value of the maximum element.  This
//    is set to 0.0 if N <= 0.
//
{
  int i;
  double rmax;
  double *r8vec_pointer;

  if ( n <= 0 )
  {
    return 0.0;
  }

  for ( i = 0; i < n; i++ )
  {
    if ( i == 0 )
    {
      rmax = *dvec;
      r8vec_pointer = dvec;
    }
    else
    {
      r8vec_pointer++;
      if ( rmax < *r8vec_pointer )
      {
        rmax = *r8vec_pointer;
      }
    }
  }

  return rmax;

}
//****************************************************************************80

double r8vec_mean ( int n, double x[] )

//****************************************************************************80
//
//  Purpose:
//
//    R8VEC_MEAN returns the mean of an R8VEC.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    01 May 1999
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, int N, the number of entries in the vector.
//
//    Input, double X[N], the vector whose mean is desired.
//
//    Output, double R8VEC_MEAN, the mean, or average, of the vector entries.
//
{
  int i;
  double mean;

  mean = 0.0;
  for ( i = 0; i < n; i++ )
  {
    mean = mean + x[i];
  }

  mean = mean / ( double ) n;

  return mean;
}
//****************************************************************************80

double r8vec_min ( int n, double *dvec )

//****************************************************************************80
//
//  Purpose:
//
//    R8VEC_MIN returns the value of the minimum element in an R8VEC.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    15 October 1998
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, int N, the number of entries in the array.
//
//    Input, double *RVEC, a pointer to the first entry of the array.
//
//    Output, double R8VEC_MIN, the value of the minimum element.  This
//    is set to 0.0 if N <= 0.
//
{
  int i;
  double rmin;
  double *r8vec_pointer;

  if ( n <= 0 )
  {
    return 0.0;
  }

  for ( i = 0; i < n; i++ )
  {
    if ( i == 0 )
    {
      rmin = *dvec;
      r8vec_pointer = dvec;
    }
    else
    {
      r8vec_pointer++;
      if ( *r8vec_pointer < rmin )
      {
        rmin = *r8vec_pointer;
      }
    }
  }

  return rmin;
}
//****************************************************************************80

void r8vec_print ( int n, double a[], string title )

//****************************************************************************80
//
//  Purpose:
//
//    R8VEC_PRINT prints an R8VEC.
//
//  Discussion:
//
//    An R8VEC is a vector of R8's.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    16 August 2004
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, int N, the number of components of the vector.
//
//    Input, double A[N], the vector to be printed.
//
//    Input, string TITLE, a title.
//
{
  int i;

  cout << "\n";
  cout << title << "\n";
  cout << "\n";
  for ( i = 0; i < n; i++ )
  {
    cout << "  " << setw(8)  << i
         << ": " << setw(14) << a[i]  << "\n";
  }

  return;
}
//****************************************************************************80

double r8vec_variance ( int n, double x[] )

//****************************************************************************80
//
//  Purpose:
//
//    R8VEC_VARIANCE returns the variance of an R8VEC.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    01 May 1999
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, int N, the number of entries in the vector.
//
//    Input, double X[N], the vector whose variance is desired.
//
//    Output, double R8VEC_VARIANCE, the variance of the vector entries.
//
{
  int i;
  double mean;
  double variance;

  mean = r8vec_mean ( n, x );

  variance = 0.0;
  for ( i = 0; i < n; i++ )
  {
    variance = variance + ( x[i] - mean ) * ( x[i] - mean );
  }

  if ( 1 < n )
  {
    variance = variance / ( double ) ( n - 1 );
  }
  else
  {
    variance = 0.0;
  }

  return variance;
}
//****************************************************************************80

void timestamp ( )

//****************************************************************************80
//
//  Purpose:
//
//    TIMESTAMP prints the current YMDHMS date as a time stamp.
//
//  Example:
//
//    31 May 2001 09:45:54 AM
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    08 July 2009
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    None
//
{
# define TIME_SIZE 40

  static char time_buffer[TIME_SIZE];
  const struct std::tm *tm_ptr;
  std::time_t now;

  now = std::time ( NULL );
  tm_ptr = std::localtime ( &now );

  std::strftime ( time_buffer, TIME_SIZE, "%d %B %Y %I:%M:%S %p", tm_ptr );

  std::cout << time_buffer << "\n";

  return;
# undef TIME_SIZE
}
//****************************************************************************80

double truncated_normal_ab_cdf ( double x, double mu, double sigma, double a, 
  double b )

//****************************************************************************80
//
//  Purpose:
//
//    TRUNCATED_NORMAL_AB_CDF evaluates the truncated Normal CDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    24 January 2017
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, double X, the argument of the CDF.
//
//    Input, double MU, SIGMA, the mean and standard deviation of the
//    parent Normal distribution.
//
//    Input, double A, B, the lower and upper truncation limits.
//
//    Output, double TRUNCATED_NORMAL_AB_CDF, the value of the CDF.
//
{
  double alpha;
  double alpha_cdf;
  double beta;
  double beta_cdf;
  double cdf;
  double xi;
  double xi_cdf;

  if ( x < a )
  {
    cdf = 0.0;
  }
  else if ( x <= b )
  {
    alpha = ( a - mu ) / sigma;
    beta = ( b - mu ) / sigma;
    xi = ( x - mu ) / sigma;

    alpha_cdf = normal_01_cdf ( alpha );
    beta_cdf = normal_01_cdf ( beta );
    xi_cdf = normal_01_cdf ( xi );

    cdf = ( xi_cdf - alpha_cdf ) / ( beta_cdf - alpha_cdf );
  }
  else
  {
    cdf = 1.0;
  }
  
  return cdf;
}
//****************************************************************************80

void truncated_normal_ab_cdf_values ( int &n_data, double &mu, double &sigma, 
  double &a, double &b, double &x, double &fx )

//****************************************************************************80
//
//  Purpose:
//
//    TRUNCATED_NORMAL_AB_CDF_VALUES: values of the Truncated Normal CDF.
//
//  Discussion:
//
//    The Normal distribution, with mean Mu and standard deviation Sigma,
//    is truncated to the interval [A,B].
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    13 September 2013
//
//  Author:
//
//    John Burkardt
//
//  Reference:
//
//    Stephen Wolfram,
//    The Mathematica Book,
//    Fourth Edition,
//    Cambridge University Press, 1999,
//    ISBN: 0-521-64314-7,
//    LC: QA76.95.W65.
//
//  Parameters:
//
//    Input/output, int &N_DATA.  The user sets N_DATA to 0 before the
//    first call.  On each call, the routine increments N_DATA by 1, and
//    returns the corresponding data; when there is no more data, the
//    output value of N_DATA will be 0 again.
//
//    Output, double &MU, the mean of the distribution.
//
//    Output, double &SIGMA, the standard deviation of the distribution.
//
//    Output, double &A, &B, the lower and upper truncation limits.
//
//    Output, double &X, the argument of the function.
//
//    Output, double &FX, the value of the function.
//
{
# define N_MAX 11

  static double a_vec[N_MAX] = {
     50.0, 
     50.0, 
     50.0, 
     50.0, 
     50.0, 
     50.0, 
     50.0, 
     50.0, 
     50.0, 
     50.0, 
     50.0 };

  static double b_vec[N_MAX] = {
     150.0, 
     150.0, 
     150.0, 
     150.0, 
     150.0, 
     150.0, 
     150.0, 
     150.0, 
     150.0, 
     150.0, 
     150.0 };

  static double fx_vec[N_MAX] = {
    0.3371694242213513, 
    0.3685009225506048, 
    0.4006444233448185, 
    0.4334107066903040, 
    0.4665988676496338, 
    0.5000000000000000, 
    0.5334011323503662, 
    0.5665892933096960, 
    0.5993555766551815, 
    0.6314990774493952, 
    0.6628305757786487 };

  static double mu_vec[N_MAX] = {
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0 };

  static double sigma_vec[N_MAX] = {
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0  };

  static double x_vec[N_MAX] = {
     90.0, 
     92.0, 
     94.0, 
     96.0, 
     98.0, 
    100.0, 
    102.0, 
    104.0, 
    106.0, 
    108.0, 
    110.0 };

  if ( n_data < 0 )
  {
    n_data = 0;
  }

  n_data = n_data + 1;

  if ( N_MAX < n_data )
  {
    n_data = 0;
    a = 0.0;
    b = 0.0;
    mu = 0.0;
    sigma = 0.0;
    x = 0.0;
    fx = 0.0;
  }
  else
  {
    a = a_vec[n_data-1];
    b = b_vec[n_data-1];
    mu = mu_vec[n_data-1];
    sigma = sigma_vec[n_data-1];
    x = x_vec[n_data-1];
    fx = fx_vec[n_data-1];
  }

  return;
# undef N_MAX
}
//****************************************************************************80

double truncated_normal_ab_cdf_inv ( double cdf, double mu, double sigma, double a, 
  double b )

//****************************************************************************80
//
//  Purpose:
//
//    TRUNCATED_NORMAL_AB_CDF_INV inverts the truncated Normal CDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    14 August 2013
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, double CDF, the value of the CDF.
//    0.0 <= CDF <= 1.0.
//
//    Input, double MU, SIGMA, the mean and standard deviation of the
//    parent Normal distribution.
//
//    Input, double A, B, the lower and upper truncation limits.
//
//    Output, double TRUNCATED_NORMAL_AB_CDF_INV, the corresponding argument.
//
{
  double alpha;
  double alpha_cdf;
  double beta;
  double beta_cdf;
  double x;
  double xi;
  double xi_cdf;

  if ( cdf < 0.0 || 1.0 < cdf )
  {
    cerr << "\n";
    cerr << "TRUNCATED_NORMAL_AB_CDF_INV - Fatal error!\n";
    cerr << "  CDF < 0 or 1 < CDF.\n";
    exit ( 1 );
  }

  alpha = ( a - mu ) / sigma;
  beta = ( b - mu ) / sigma;

  alpha_cdf = normal_01_cdf ( alpha );
  beta_cdf = normal_01_cdf ( beta );

  xi_cdf = ( beta_cdf - alpha_cdf ) * cdf + alpha_cdf;
  xi = normal_01_cdf_inv ( xi_cdf );

  x = mu + sigma * xi;

  return x;
}
//****************************************************************************80

double truncated_normal_ab_mean ( double mu, double sigma, double a, double b )

//****************************************************************************80
//
//  Purpose:
//
//    TRUNCATED_NORMAL_AB_MEAN returns the mean of the truncated Normal PDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    14 August 2013
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, double MU, SIGMA, the mean and standard deviation of the
//    parent Normal distribution.
//
//    Input, double A, B, the lower and upper truncation limits.
//
//    Output, double TRUNCATED_NORMAL_AB_MEAN, the mean of the PDF.
//
{
  double alpha;
  double alpha_cdf;
  double alpha_pdf;
  double beta;
  double beta_cdf;
  double beta_pdf;
  double mean;

  alpha = ( a - mu ) / sigma;
  beta = ( b - mu ) / sigma;

  alpha_cdf = normal_01_cdf ( alpha );
  beta_cdf = normal_01_cdf ( beta  );

  alpha_pdf = normal_01_pdf ( alpha );
  beta_pdf = normal_01_pdf ( beta );

  mean = mu + sigma * ( alpha_pdf - beta_pdf ) / ( beta_cdf - alpha_cdf );

  return mean;
}
//****************************************************************************80

double truncated_normal_ab_moment ( int order, double mu, double sigma, 
  double a, double b )

//****************************************************************************80
//
//  Purpose:
//
//    TRUNCATED_NORMAL_AB_MOMENT: moments of the truncated Normal PDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    11 September 2013
//
//  Author:
//
//    John Burkardt
//
//  Reference:
//
//    Phoebus Dhrymes,
//    Moments of Truncated Normal Distributions,
//    May 2005.
//
//  Parameters:
//
//    Input, int ORDER, the order of the moment.
//    0 <= ORDER.
//
//    Input, double MU, SIGMA, the mean and standard deviation of the
//    parent Normal distribution.
//    0.0 < S.
//
//    Input, double A, B, the lower and upper truncation limits.
//    A < B.
//
//    Output, double TRUNCATED_NORMAL_AB_MOMENT, the moment of the PDF.
//
{
  double a_cdf;
  double a_h;
  double a_pdf;
  double b_cdf;
  double b_h;
  double b_pdf;
  double ir;
  double irm1;
  double irm2;
  double moment;
  int r;

  if ( order < 0 )
  {
    cerr << "\n";
    cerr << "TRUNCATED_NORMAL_AB_MOMENT - Fatal error!\n";
    cerr << "  ORDER < 0.\n";
    exit ( 1 );
  }

  if ( sigma <= 0.0 )
  {
    cerr << "\n";
    cerr << "TRUNCATED_NORMAL_AB_MOMENT - Fatal error!\n";
    cerr << "  SIGMA <= 0.0.\n";
    exit ( 1 );
  }

  if ( b <= a )
  {
    cerr << "\n";
    cerr << "TRUNCATED_NORMAL_AB_MOMENT - Fatal error!\n";
    cerr << "  B <= A.\n";
    exit ( 1 );
  }

  a_h = ( a - mu ) / sigma;
  a_pdf = normal_01_pdf ( a_h );
  a_cdf = normal_01_cdf ( a_h );

  if ( a_cdf == 0.0 )
  {
    cerr << "\n";
    cerr << "TRUNCATED_NORMAL_AB_MOMENT - Fatal error!\n";
    cerr << "  PDF/CDF ratio fails, because A_CDF too small.\n";
    cerr << "  A_PDF = " << a_pdf << "\n";
    cerr << "  A_CDF = " << a_cdf << "\n";
    exit ( 1 );
  }

  b_h = ( b - mu ) / sigma;
  b_pdf = normal_01_pdf ( b_h );
  b_cdf = normal_01_cdf ( b_h );

  if ( b_cdf == 0.0 )
  {
    cerr << "\n";
    cerr << "TRUNCATED_NORMAL_AB_MOMENT - Fatal error!\n";
    cerr << "  PDF/CDF ratio fails, because B_CDF too small.\n";
    cerr << "  B_PDF = " << b_pdf << "\n";
    cerr << "  B_CDF = " << b_cdf << "\n";
    exit ( 1 );
  }

  moment = 0.0;
  irm2 = 0.0;
  irm1 = 0.0;

  for ( r = 0; r <= order; r++ )
  {
    if ( r == 0 )
    {
      ir = 1.0;
    }
    else if ( r == 1 )
    {
      ir = - ( b_pdf - a_pdf ) / ( b_cdf - a_cdf );
    }
    else
    {
      ir = ( double ) ( r - 1 ) * irm2 
        - ( pow ( b_h, r - 1 ) * b_pdf - pow ( a_h, r - 1 ) * a_pdf )
        / ( b_cdf - a_cdf );
    }

    moment = moment + r8_choose ( order, r ) * pow ( mu, order - r ) 
      * pow ( sigma, r ) * ir;

    irm2 = irm1;
    irm1 = ir;
  }

  return moment;
}
//****************************************************************************80

double truncated_normal_ab_pdf ( double x, double mu, double sigma, double a, 
  double b )

//****************************************************************************80
//
//  Purpose:
//
//    TRUNCATED_NORMAL_AB_PDF evaluates the truncated Normal PDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    24 January 2017
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, double X, the argument of the PDF.
//
//    Input, double MU, SIGMA, the mean and standard deviation of the
//    parent Normal distribution.
//
//    Input, double A, B, the lower and upper truncation limits.
//
//    Output, double TRUNCATED_NORMAL_AB_PDF, the value of the PDF.
//
{
  double alpha;
  double alpha_cdf;
  double beta;
  double beta_cdf;
  double pdf;
  double xi;
  double xi_pdf;

  if ( x < a )
  {
    pdf = 0.0;
  }
  else if ( x <= b )
  {
    alpha = ( a - mu ) / sigma;
    beta = ( b - mu ) / sigma;
    xi = ( x - mu ) / sigma;

    alpha_cdf = normal_01_cdf ( alpha );
    beta_cdf = normal_01_cdf ( beta );
    xi_pdf = normal_01_pdf ( xi );

    pdf = xi_pdf / ( beta_cdf - alpha_cdf ) / sigma;
  }
  else
  {
    pdf = 0.0;
  }
  
  return pdf;
}
//****************************************************************************80

void truncated_normal_ab_pdf_values ( int &n_data, double &mu, double &sigma, 
  double &a, double &b, double &x, double &fx )

//****************************************************************************80
//
//  Purpose:
//
//    TRUNCATED_NORMAL_AB_PDF_VALUES: values of the Truncated Normal PDF.
//
//  Discussion:
//
//    The Normal distribution, with mean Mu and standard deviation Sigma,
//    is truncated to the interval [A,B].
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    13 September 2013
//
//  Author:
//
//    John Burkardt
//
//  Reference:
//
//    Stephen Wolfram,
//    The Mathematica Book,
//    Fourth Edition,
//    Cambridge University Press, 1999,
//    ISBN: 0-521-64314-7,
//    LC: QA76.95.W65.
//
//  Parameters:
//
//    Input/output, int &N_DATA.  The user sets N_DATA to 0 before the
//    first call.  On each call, the routine increments N_DATA by 1, and
//    returns the corresponding data; when there is no more data, the
//    output value of N_DATA will be 0 again.
//
//    Output, double &MU, the mean of the distribution.
//
//    Output, double &SIGMA, the standard deviation of the distribution.
//
//    Output, double &A, &B, the lower and upper truncation limits.
//
//    Output, double &X, the argument of the function.
//
//    Output, double &FX, the value of the function.
//
{
# define N_MAX 11

  static double a_vec[N_MAX] = {
     50.0, 
     50.0, 
     50.0, 
     50.0, 
     50.0, 
     50.0, 
     50.0, 
     50.0, 
     50.0, 
     50.0, 
     50.0 };

  static double b_vec[N_MAX] = {
     150.0, 
     150.0, 
     150.0, 
     150.0, 
     150.0, 
     150.0, 
     150.0, 
     150.0, 
     150.0, 
     150.0, 
     150.0 };

  static double fx_vec[N_MAX] = {
    0.01543301171801836,
    0.01588394472270638,
    0.01624375997031919,
    0.01650575046469259,
    0.01666496869385951,
    0.01671838200940538,
    0.01666496869385951,
    0.01650575046469259,
    0.01624375997031919,
    0.01588394472270638,
    0.01543301171801836 };

  static double mu_vec[N_MAX] = {
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0 };

  static double sigma_vec[N_MAX] = {
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0  };

  static double x_vec[N_MAX] = {
     90.0, 
     92.0, 
     94.0, 
     96.0, 
     98.0, 
    100.0, 
    102.0, 
    104.0, 
    106.0, 
    108.0, 
    110.0 };

  if ( n_data < 0 )
  {
    n_data = 0;
  }

  n_data = n_data + 1;

  if ( N_MAX < n_data )
  {
    n_data = 0;
    a = 0.0;
    b = 0.0;
    mu = 0.0;
    sigma = 0.0;
    x = 0.0;
    fx = 0.0;
  }
  else
  {
    a = a_vec[n_data-1];
    b = b_vec[n_data-1];
    mu = mu_vec[n_data-1];
    sigma = sigma_vec[n_data-1];
    x = x_vec[n_data-1];
    fx = fx_vec[n_data-1];
  }

  return;
# undef N_MAX
}
//****************************************************************************80

double truncated_normal_ab_sample ( double mu, double sigma, double a, double b, 
  int &seed )

//****************************************************************************80
//
//  Purpose:
//
//    TRUNCATED_NORMAL_AB_SAMPLE samples the truncated Normal PDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    14 August 2013
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, double MU, SIGMA, the mean and standard deviation of the
//    parent Normal distribution.
//
//    Input, double A, B, the lower and upper truncation limits.
//
//    Input/output, int &SEED, a seed for the random number
//    generator.
//
//    Output, double TRUNCATED_NORMAL_AB_SAMPLE, a sample of the PDF.
//
{
  double alpha;
  double alpha_cdf;
  double beta;
  double beta_cdf;
  double u;
  double x;
  double xi;
  double xi_cdf;

  alpha = ( a - mu ) / sigma;
  beta = ( b - mu ) / sigma;

  alpha_cdf = normal_01_cdf ( alpha );
  beta_cdf = normal_01_cdf ( beta );

  u = r8_uniform_01 ( seed );
  xi_cdf = alpha_cdf + u * ( beta_cdf - alpha_cdf );
  xi = normal_01_cdf_inv ( xi_cdf );

  x = mu + sigma * xi;

  return x;
}
//****************************************************************************80

double truncated_normal_ab_variance ( double mu, double sigma, double a, 
  double b )

//****************************************************************************80
//
//  Purpose:
//
//    TRUNCATED_NORMAL_AB_VARIANCE returns the variance of the truncated Normal PDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    14 August 2013
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, double MU, SIGMA, the mean and standard deviation of the
//    parent Normal distribution.
//
//    Input, double A, B, the lower and upper truncation limits.
//
//    Output, double TRUNCATED_NORMAL_AB_VARIANCE, the variance of the PDF.
//
{
  double alpha;
  double alpha_cdf;
  double alpha_pdf;
  double beta;
  double beta_cdf;
  double beta_pdf;
  double variance;

  alpha = ( a - mu ) / sigma;
  beta = ( b - mu ) / sigma;

  alpha_pdf = normal_01_pdf ( alpha );
  beta_pdf = normal_01_pdf ( beta );

  alpha_cdf = normal_01_cdf ( alpha );
  beta_cdf = normal_01_cdf ( beta );

  variance = sigma * sigma * ( 1.0 
    + ( alpha * alpha_pdf - beta * beta_pdf ) / ( beta_cdf - alpha_cdf ) 
    - pow ( ( alpha_pdf - beta_pdf ) / ( beta_cdf - alpha_cdf ), 2 ) );

  return variance;
}
//****************************************************************************80

double truncated_normal_a_cdf ( double x, double mu, double sigma, double a )

//****************************************************************************80
//
//  Purpose:
//
//    TRUNCATED_NORMAL_A_CDF evaluates the lower truncated Normal CDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    24 January 2017
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, double X, the argument of the CDF.
//
//    Input, double MU, SIGMA, the mean and standard deviation of the
//    parent Normal distribution.
//
//    Input, double A, the lower truncation limit.
//
//    Output, double TRUNCATED_NORMAL_A_CDF, the value of the CDF.
//
{
  double alpha;
  double alpha_cdf;
  double cdf;
  double xi;
  double xi_cdf;

  if ( x < a )
  {
    cdf = 0.0;
  }
  else
  {
    alpha = ( a - mu ) / sigma;
    xi = ( x - mu ) / sigma;

    alpha_cdf = normal_01_cdf ( alpha );
    xi_cdf = normal_01_cdf ( xi );

    cdf = ( xi_cdf - alpha_cdf ) / ( 1.0 - alpha_cdf );
  }
  
  return cdf;
}
//****************************************************************************80

void truncated_normal_a_cdf_values ( int &n_data, double &mu, double &sigma, 
  double &a, double &x, double &fx )

//****************************************************************************80
//
//  Purpose:
//
//    TRUNCATED_NORMAL_A_CDF_VALUES: values of the Lower Truncated Normal CDF.
//
//  Discussion:
//
//    The Normal distribution, with mean Mu and standard deviation Sigma,
//    is truncated to the interval [A,+oo).
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    14 September 2013
//
//  Author:
//
//    John Burkardt
//
//  Reference:
//
//    Stephen Wolfram,
//    The Mathematica Book,
//    Fourth Edition,
//    Cambridge University Press, 1999,
//    ISBN: 0-521-64314-7,
//    LC: QA76.95.W65.
//
//  Parameters:
//
//    Input/output, int &N_DATA.  The user sets N_DATA to 0 before the
//    first call.  On each call, the routine increments N_DATA by 1, and
//    returns the corresponding data; when there is no more data, the
//    output value of N_DATA will be 0 again.
//
//    Output, double &MU, the mean of the distribution.
//
//    Output, double &SIGMA, the standard deviation of the distribution.
//
//    Output, double &A, the lower truncation limit.
//
//    Output, double &X, the argument of the function.
//
//    Output, double &FX, the value of the function.
//
{
# define N_MAX 11

  static double a_vec[N_MAX] = {
     50.0, 
     50.0, 
     50.0, 
     50.0, 
     50.0, 
     50.0, 
     50.0, 
     50.0, 
     50.0, 
     50.0, 
     50.0 };

  static double fx_vec[N_MAX] = {
    0.3293202045481688, 
    0.3599223134505957, 
    0.3913175216041539, 
    0.4233210140873113, 
    0.4557365629792204, 
    0.4883601253415709, 
    0.5209836877039214, 
    0.5533992365958304, 
    0.5854027290789878, 
    0.6167979372325460, 
    0.6474000461349729 };

  static double mu_vec[N_MAX] = {
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0 };

  static double sigma_vec[N_MAX] = {
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0  };

  static double x_vec[N_MAX] = {
     90.0, 
     92.0, 
     94.0, 
     96.0, 
     98.0, 
    100.0, 
    102.0, 
    104.0, 
    106.0, 
    108.0, 
    110.0 };

  if ( n_data < 0 )
  {
    n_data = 0;
  }

  n_data = n_data + 1;

  if ( N_MAX < n_data )
  {
    n_data = 0;
    a = 0.0;
    mu = 0.0;
    sigma = 0.0;
    x = 0.0;
    fx = 0.0;
  }
  else
  {
    a = a_vec[n_data-1];
    mu = mu_vec[n_data-1];
    sigma = sigma_vec[n_data-1];
    x = x_vec[n_data-1];
    fx = fx_vec[n_data-1];
  }

  return;
# undef N_MAX
}
//****************************************************************************80

double truncated_normal_a_cdf_inv ( double cdf, double mu, double sigma, 
  double a )

//****************************************************************************80
//
//  Purpose:
//
//    TRUNCATED_NORMAL_A_CDF_INV inverts the lower truncated Normal CDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    21 August 2013
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, double CDF, the value of the CDF.
//    0.0 <= CDF <= 1.0.
//
//    Input, double MU, SIGMA, the mean and standard deviation of the
//    parent Normal distribution.
//
//    Input, double A, the lower truncation limit.
//
//    Output, double TRUNCATED_NORMAL_A_CDF_INV, the corresponding argument.
//
{
  double alpha;
  double alpha_cdf;
  double x;
  double xi;
  double xi_cdf;

  if ( cdf < 0.0 || 1.0 < cdf )
  {
    cerr << "\n";
    cerr << "TRUNCATED_NORMAL_A_CDF_INV - Fatal error!\n";
    cerr << "  CDF < 0 or 1 < CDF.\n";
    exit ( 1 );
  }

  alpha = ( a - mu ) / sigma;

  alpha_cdf = normal_01_cdf ( alpha );

  xi_cdf = ( 1.0 - alpha_cdf ) * cdf + alpha_cdf;
  xi = normal_01_cdf_inv ( xi_cdf );

  x = mu + sigma * xi;

  return x;
}
//****************************************************************************80

double truncated_normal_a_mean ( double mu, double sigma, double a )

//****************************************************************************80
//
//  Purpose:
//
//    TRUNCATED_NORMAL_A_MEAN returns the mean of the lower truncated Normal PDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    21 August 2013
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, double MU, SIGMA, the mean and standard deviation of the
//    parent Normal distribution.
//
//    Input, double A, the lower truncation limit.
//
//    Output, double TRUNCATED_NORMAL_A_MEAN, the mean of the PDF.
//
{
  double alpha;
  double alpha_cdf;
  double alpha_pdf;
  double mean;

  alpha = ( a - mu ) / sigma;

  alpha_cdf = normal_01_cdf ( alpha );

  alpha_pdf = normal_01_pdf ( alpha );

  mean = mu + sigma * alpha_pdf / ( 1.0 - alpha_cdf );

  return mean;
}
//****************************************************************************80

double truncated_normal_a_moment ( int order, double mu, double sigma, 
  double a )

//****************************************************************************80
//
//  Purpose:
//
//    TRUNCATED_NORMAL_A_MOMENT: moments of the lower truncated Normal PDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    11 September 2013
//
//  Author:
//
//    John Burkardt
//
//  Reference:
//
//    Phoebus Dhrymes,
//    Moments of Truncated Normal Distributions,
//    May 2005.
//
//  Parameters:
//
//    Input, int ORDER, the order of the moment.
//    0 <= ORDER.
//
//    Input, double MU, SIGMA, the mean and standard deviation of the
//    parent Normal distribution.
//
//    Input, double A, the lower truncation limit.
//
//    Output, double TRUNCATED_NORMAL_A_MOMENT, the moment of the PDF.
//
{
  double moment;

  moment = r8_mop ( order )
    * truncated_normal_b_moment ( order, - mu, sigma, - a );

  return moment;
}
//****************************************************************************80

double truncated_normal_a_pdf ( double x, double mu, double sigma, double a )

//****************************************************************************80
//
//  Purpose:
//
//    TRUNCATED_NORMAL_A_PDF evaluates the lower truncated Normal PDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    24 January 2017
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, double X, the argument of the PDF.
//
//    Input, double MU, SIGMA, the mean and standard deviation of the
//    parent Normal distribution.
//
//    Input, double A, the lower truncation limit.
//
//    Output, double TRUNCATED_NORMAL_A_PDF, the value of the PDF.
//
{
  double alpha;
  double alpha_cdf;
  double pdf;
  double xi;
  double xi_pdf;

  if ( x < a )
  {
    pdf = 0.0;
  }
  else
  {
    alpha = ( a - mu ) / sigma;
    xi = ( x - mu ) / sigma;

    alpha_cdf = normal_01_cdf ( alpha );
    xi_pdf = normal_01_pdf ( xi );

    pdf = xi_pdf / ( 1.0 - alpha_cdf ) / sigma;
  }
  
  return pdf;
}
//****************************************************************************80

void truncated_normal_a_pdf_values ( int &n_data, double &mu, double &sigma, 
  double &a, double &x, double &fx )

//****************************************************************************80
//
//  Purpose:
//
//    TRUNCATED_NORMAL_A_PDF_VALUES: values of the Lower Truncated Normal PDF.
//
//  Discussion:
//
//    The Normal distribution, with mean Mu and standard deviation Sigma,
//    is truncated to the interval [A,+oo).
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    14 September 2013
//
//  Author:
//
//    John Burkardt
//
//  Reference:
//
//    Stephen Wolfram,
//    The Mathematica Book,
//    Fourth Edition,
//    Cambridge University Press, 1999,
//    ISBN: 0-521-64314-7,
//    LC: QA76.95.W65.
//
//  Parameters:
//
//    Input/output, int &N_DATA.  The user sets N_DATA to 0 before the
//    first call.  On each call, the routine increments N_DATA by 1, and
//    returns the corresponding data; when there is no more data, the
//    output value of N_DATA will be 0 again.
//
//    Output, double &MU, the mean of the distribution.
//
//    Output, double &SIGMA, the standard deviation of the distribution.
//
//    Output, double &A, the lower truncation limit.
//
//    Output, double &X, the argument of the function.
//
//    Output, double &FX, the value of the function.
//
{
# define N_MAX 11

  static double a_vec[N_MAX] = {
     50.0, 
     50.0, 
     50.0, 
     50.0, 
     50.0, 
     50.0, 
     50.0, 
     50.0, 
     50.0, 
     50.0, 
     50.0 };

  static double fx_vec[N_MAX] = {
     0.01507373507401876,
     0.01551417047139894,
     0.01586560931024694,
     0.01612150073158793,
     0.01627701240029317,
     0.01632918226724295,
     0.01627701240029317,
     0.01612150073158793,
     0.01586560931024694,
     0.01551417047139894,
     0.01507373507401876 };

  static double mu_vec[N_MAX] = {
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0 };

  static double sigma_vec[N_MAX] = {
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0  };

  static double x_vec[N_MAX] = {
     90.0, 
     92.0, 
     94.0, 
     96.0, 
     98.0, 
    100.0, 
    102.0, 
    104.0, 
    106.0, 
    108.0, 
    110.0 };

  if ( n_data < 0 )
  {
    n_data = 0;
  }

  n_data = n_data + 1;

  if ( N_MAX < n_data )
  {
    n_data = 0;
    a = 0.0;
    mu = 0.0;
    sigma = 0.0;
    x = 0.0;
    fx = 0.0;
  }
  else
  {
    a = a_vec[n_data-1];
    mu = mu_vec[n_data-1];
    sigma = sigma_vec[n_data-1];
    x = x_vec[n_data-1];
    fx = fx_vec[n_data-1];
  }

  return;
# undef N_MAX
}
//****************************************************************************80

double truncated_normal_a_sample ( double mu, double sigma, double a, 
  int &seed )

//****************************************************************************80
//
//  Purpose:
//
//    TRUNCATED_NORMAL_A_SAMPLE samples the lower truncated Normal PDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    21 August 2013
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, double MU, SIGMA, the mean and standard deviation of the
//    parent Normal distribution.
//
//    Input, double A, the lower truncation limit.
//
//    Input/output, int &SEED, a seed for the random number
//    generator.
//
//    Output, double TRUNCATED_NORMAL_A_SAMPLE, a sample of the PDF.
//
{
  double alpha;
  double alpha_cdf;
  double u;
  double x;
  double xi;
  double xi_cdf;

  alpha = ( a - mu ) / sigma;

  alpha_cdf = normal_01_cdf ( alpha );

  u = r8_uniform_01 ( seed );
  xi_cdf = alpha_cdf + u * ( 1.0 - alpha_cdf );
  xi = normal_01_cdf_inv ( xi_cdf );

  x = mu + sigma * xi;

  return x;
}
//****************************************************************************80

double truncated_normal_a_variance ( double mu, double sigma, double a )

//****************************************************************************80
//
//  Purpose:
//
//    TRUNCATED_NORMAL_A_VARIANCE: variance of the lower truncated Normal PDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    21 August 2013
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, double MU, SIGMA, the mean and standard deviation of the
//    parent Normal distribution.
//
//    Input, double A, the lower truncation limit.
//
//    Output, double TRUNCATED_NORMAL_A_VARIANCE, the variance of the PDF.
//
{
  double alpha;
  double alpha_cdf;
  double alpha_pdf;
  double variance;

  alpha = ( a - mu ) / sigma;

  alpha_pdf = normal_01_pdf ( alpha );

  alpha_cdf = normal_01_cdf ( alpha );

  variance = sigma * sigma * ( 1.0 
    + alpha * alpha_pdf / ( 1.0 - alpha_cdf ) 
    - pow ( alpha_pdf / ( 1.0 - alpha_cdf ), 2 ) );

  return variance;
}
//****************************************************************************80

double truncated_normal_b_cdf ( double x, double mu, double sigma, double b )

//****************************************************************************80
//
//  Purpose:
//
//    TRUNCATED_NORMAL_B_CDF evaluates the upper truncated Normal CDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    24 January 2017
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, double X, the argument of the CDF.
//
//    Input, double MU, SIGMA, the mean and standard deviation of the
//    parent Normal distribution.
//
//    Input, double B, the upper truncation limit.
//
//    Output, double TRUNCATED_NORMAL_B_CDF, the value of the CDF.
//
{
  double beta;
  double beta_cdf;
  double cdf;
  double xi;
  double xi_cdf;

  if ( x <= b )
  {
    beta = ( b - mu ) / sigma;
    xi = ( x - mu ) / sigma;

    beta_cdf = normal_01_cdf ( beta );
    xi_cdf = normal_01_cdf ( xi );

    cdf = xi_cdf / beta_cdf;
  }
  else
  {
    cdf = 1.0;
  }
  
  return cdf;
}
//****************************************************************************80

void truncated_normal_b_cdf_values ( int &n_data, double &mu, double &sigma, 
  double &b, double &x, double &fx )

//****************************************************************************80
//
//  Purpose:
//
//    TRUNCATED_NORMAL_B_CDF_VALUES: values of the upper Truncated Normal CDF.
//
//  Discussion:
//
//    The Normal distribution, with mean Mu and standard deviation Sigma,
//    is truncated to the interval (-oo,B].
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    14 September 2013
//
//  Author:
//
//    John Burkardt
//
//  Reference:
//
//    Stephen Wolfram,
//    The Mathematica Book,
//    Fourth Edition,
//    Cambridge University Press, 1999,
//    ISBN: 0-521-64314-7,
//    LC: QA76.95.W65.
//
//  Parameters:
//
//    Input/output, int &N_DATA.  The user sets N_DATA to 0 before the
//    first call.  On each call, the routine increments N_DATA by 1, and
//    returns the corresponding data; when there is no more data, the
//    output value of N_DATA will be 0 again.
//
//    Output, double &MU, the mean of the distribution.
//
//    Output, double &SIGMA, the standard deviation of the distribution.
//
//    Output, double &B, the upper truncation limit.
//
//    Output, double &X, the argument of the function.
//
//    Output, double &FX, the value of the function.
//
{
# define N_MAX 11

  static double b_vec[N_MAX] = {
     150.0, 
     150.0, 
     150.0, 
     150.0, 
     150.0, 
     150.0, 
     150.0, 
     150.0, 
     150.0, 
     150.0, 
     150.0 };

  static double fx_vec[N_MAX] = {
    0.3525999538650271, 
    0.3832020627674540, 
    0.4145972709210122, 
    0.4466007634041696, 
    0.4790163122960786, 
    0.5116398746584291, 
    0.5442634370207796, 
    0.5766789859126887, 
    0.6086824783958461, 
    0.6400776865494043, 
    0.6706797954518312 };

  static double mu_vec[N_MAX] = {
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0 };

  static double sigma_vec[N_MAX] = {
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0  };

  static double x_vec[N_MAX] = {
     90.0, 
     92.0, 
     94.0, 
     96.0, 
     98.0, 
    100.0, 
    102.0, 
    104.0, 
    106.0, 
    108.0, 
    110.0 };

  if ( n_data < 0 )
  {
    n_data = 0;
  }

  n_data = n_data + 1;

  if ( N_MAX < n_data )
  {
    n_data = 0;
    b = 0.0;
    mu = 0.0;
    sigma = 0.0;
    x = 0.0;
    fx = 0.0;
  }
  else
  {
    b = b_vec[n_data-1];
    mu = mu_vec[n_data-1];
    sigma = sigma_vec[n_data-1];
    x = x_vec[n_data-1];
    fx = fx_vec[n_data-1];
  }

  return;
# undef N_MAX
}
//****************************************************************************80

double truncated_normal_b_cdf_inv ( double cdf, double mu, double sigma, 
  double b )

//****************************************************************************80
//
//  Purpose:
//
//    TRUNCATED_NORMAL_B_CDF_INV inverts the upper truncated Normal CDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    21 August 2013
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, double CDF, the value of the CDF.
//    0.0 <= CDF <= 1.0.
//
//    Input, double MU, SIGMA, the mean and standard deviation of the
//    parent Normal distribution.
//
//    Input, double B, the upper truncation limit.
//
//    Output, double TRUNCATED_NORMAL_B_CDF_INV, the corresponding argument.
//
{
  double beta;
  double beta_cdf;
  double x;
  double xi;
  double xi_cdf;

  if ( cdf < 0.0 || 1.0 < cdf )
  {
    cerr << "\n";
    cerr << "TRUNCATED_NORMAL_B_CDF_INV - Fatal error!\n";
    cerr << "  CDF < 0 or 1 < CDF.\n";
    exit ( 1 );
  }

  beta = ( b - mu ) / sigma;

  beta_cdf = normal_01_cdf ( beta );

  xi_cdf = beta_cdf * cdf;
  xi = normal_01_cdf_inv ( xi_cdf );

  x = mu + sigma * xi;

  return x;
}
//****************************************************************************80

double truncated_normal_b_mean ( double mu, double sigma, double b )

//****************************************************************************80
//
//  Purpose:
//
//    TRUNCATED_NORMAL_B_MEAN returns the mean of the upper truncated Normal PDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    21 August 2013
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, double MU, SIGMA, the mean and standard deviation of the
//    parent Normal distribution.
//
//    Input, double B, the upper truncation limit.
//
//    Output, double TRUNCATED_NORMAL_B_MEAN, the mean of the PDF.
//
{
  double beta;
  double beta_cdf;
  double beta_pdf;
  double mean;

  beta = ( b - mu ) / sigma;

  beta_cdf = normal_01_cdf ( beta );

  beta_pdf = normal_01_pdf ( beta );

  mean = mu - sigma * beta_pdf / beta_cdf;

  return mean;
}
//****************************************************************************80

double truncated_normal_b_moment ( int order, double mu, double sigma, double b )

//****************************************************************************80
//
//  Purpose:
//
//    TRUNCATED_NORMAL_B_MOMENT: moments of the upper truncated Normal PDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    11 September 2013
//
//  Author:
//
//    John Burkardt
//
//  Reference:
//
//    Phoebus Dhrymes,
//    Moments of Truncated Normal Distributions,
//    May 2005.
//
//  Parameters:
//
//    Input, int ORDER, the order of the moment.
//    0 <= ORDER.
//
//    Input, double MU, SIGMA, the mean and standard deviation of the
//    parent Normal distribution.
//
//    Input, double B, the upper truncation limit.
//
//    Output, double TRUNCATED_NORMAL_B_MOMENT, the moment of the PDF.
//
{
  double f;
  double h;
  double h_cdf;
  double h_pdf;
  double ir;
  double irm1;
  double irm2;
  double moment;
  int r;

  if ( order < 0 )
  {
    cerr << "\n";
    cerr << "TRUNCATED_NORMAL_B_MOMENT - Fatal error!\n";
    cerr << "  ORDER < 0.\n";
    exit ( 1 );
  }

  h = ( b - mu ) / sigma;
  h_pdf = normal_01_pdf ( h );
  h_cdf = normal_01_cdf ( h );

  if ( h_cdf == 0.0 )
  {
    cerr << "\n";
    cerr << "TRUNCATED_NORMAL_B_MOMENT - Fatal error!\n";
    cerr << "  CDF((B-MU)/SIGMA) = 0.\n";
    exit ( 1 );
  }

  f = h_pdf / h_cdf;

  moment = 0.0;
  irm2 = 0.0;
  irm1 = 0.0;

  for ( r = 0; r <= order; r++ )
  {
    if ( r == 0 )
    {
      ir = 1.0;
    }
    else if ( r == 1 )
    {
      ir = - f;
    }
    else
    {
      ir = - pow ( h, r - 1 ) * f + ( double ) ( r - 1 ) * irm2;
    }

    moment = moment + r8_choose ( order, r ) * pow ( mu, order - r ) 
      * pow ( sigma, r ) * ir;

    irm2 = irm1;
    irm1 = ir;
  }

  return moment;
}
//****************************************************************************80

double truncated_normal_b_pdf ( double x, double mu, double sigma, double b )

//****************************************************************************80
//
//  Purpose:
//
//    TRUNCATED_NORMAL_B_PDF evaluates the upper truncated Normal PDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    24 January 2017
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, double X, the argument of the PDF.
//
//    Input, double MU, SIGMA, the mean and standard deviation of the
//    parent Normal distribution.
//
//    Input, double B, the upper truncation limit.
//
//    Output, double TRUNCATED_NORMAL_B_PDF, the value of the PDF.
//
{
  double beta;
  double beta_cdf;
  double pdf;
  double xi;
  double xi_pdf;

  if ( x <= b )
  {
    beta = ( b - mu ) / sigma;
    xi = ( x - mu ) / sigma;

    beta_cdf = normal_01_cdf ( beta );
    xi_pdf = normal_01_pdf ( xi );

    pdf = xi_pdf / beta_cdf / sigma;
  }
  else
  {
    pdf = 0.0;
  }
  
  return pdf;
}
//****************************************************************************80

void truncated_normal_b_pdf_values ( int &n_data, double &mu, double &sigma, 
  double &b, double &x, double &fx )

//****************************************************************************80
//
//  Purpose:
//
//    TRUNCATED_NORMAL_B_PDF_VALUES: values of the Upper Truncated Normal PDF.
//
//  Discussion:
//
//    The Normal distribution, with mean Mu and standard deviation Sigma,
//    is truncated to the interval (-oo,B].
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    14 September 2013
//
//  Author:
//
//    John Burkardt
//
//  Reference:
//
//    Stephen Wolfram,
//    The Mathematica Book,
//    Fourth Edition,
//    Cambridge University Press, 1999,
//    ISBN: 0-521-64314-7,
//    LC: QA76.95.W65.
//
//  Parameters:
//
//    Input/output, int &N_DATA.  The user sets N_DATA to 0 before the
//    first call.  On each call, the routine increments N_DATA by 1, and
//    returns the corresponding data; when there is no more data, the
//    output value of N_DATA will be 0 again.
//
//    Output, double &MU, the mean of the distribution.
//
//    Output, double &SIGMA, the standard deviation of the distribution.
//
//    Output, double &B, the upper truncation limit.
//
//    Output, double &X, the argument of the function.
//
//    Output, double &FX, the value of the function.
//
{
# define N_MAX 11

  static double b_vec[N_MAX] = {
     150.0, 
     150.0, 
     150.0, 
     150.0, 
     150.0, 
     150.0, 
     150.0, 
     150.0, 
     150.0, 
     150.0, 
     150.0 };

  static double fx_vec[N_MAX] = {
    0.01507373507401876, 
    0.01551417047139894, 
    0.01586560931024694, 
    0.01612150073158793, 
    0.01627701240029317, 
    0.01632918226724295, 
    0.01627701240029317, 
    0.01612150073158793, 
    0.01586560931024694, 
    0.01551417047139894, 
    0.01507373507401876 };

  static double mu_vec[N_MAX] = {
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0, 
     100.0 };

  static double sigma_vec[N_MAX] = {
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0, 
    25.0  };

  static double x_vec[N_MAX] = {
     90.0, 
     92.0, 
     94.0, 
     96.0, 
     98.0, 
    100.0, 
    102.0, 
    104.0, 
    106.0, 
    108.0, 
    110.0 };

  if ( n_data < 0 )
  {
    n_data = 0;
  }

  n_data = n_data + 1;

  if ( N_MAX < n_data )
  {
    n_data = 0;
    b = 0.0;
    mu = 0.0;
    sigma = 0.0;
    x = 0.0;
    fx = 0.0;
  }
  else
  {
    b = b_vec[n_data-1];
    mu = mu_vec[n_data-1];
    sigma = sigma_vec[n_data-1];
    x = x_vec[n_data-1];
    fx = fx_vec[n_data-1];
  }

  return;
# undef N_MAX
}
//****************************************************************************80

double truncated_normal_b_sample ( double mu, double sigma, double b, int &seed )

//****************************************************************************80
//
//  Purpose:
//
//    TRUNCATED_NORMAL_B_SAMPLE samples the upper truncated Normal PDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    21 August 2013
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, double MU, SIGMA, the mean and standard deviation of the
//    parent Normal distribution.
//
//    Input, double B, the upper truncation limit.
//
//    Input/output, int &SEED, a seed for the random number
//    generator.
//
//    Output, double TRUNCATED_NORMAL_B_SAMPLE, a sample of the PDF.
//
{
  double beta;
  double beta_cdf;
  double u;
  double x;
  double xi;
  double xi_cdf;

  beta = ( b - mu ) / sigma;

  beta_cdf = normal_01_cdf ( beta );

  u = r8_uniform_01 ( seed );
  xi_cdf = u * beta_cdf;
  xi = normal_01_cdf_inv ( xi_cdf );

  x = mu + sigma * xi;

  return x;
}
//****************************************************************************80

double truncated_normal_b_variance ( double mu, double sigma, double b )

//****************************************************************************80
//
//  Purpose:
//
//    TRUNCATED_NORMAL_B_VARIANCE: variance of the upper truncated Normal PDF.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    14 August 2013
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, double MU, SIGMA, the mean and standard deviation of the
//    parent Normal distribution.
//
//    Input, double B, the upper truncation limit.
//
//    Output, double TRUNCATED_NORMAL_B_VARIANCE, the variance of the PDF.
//
{
  double beta;
  double beta_cdf;
  double beta_pdf;
  double variance;

  beta = ( b - mu ) / sigma;

  beta_pdf = normal_01_pdf ( beta );

  beta_cdf = normal_01_cdf ( beta );

  variance = sigma * sigma * ( 1.0 
    - beta * beta_pdf / beta_cdf 
    - pow ( beta_pdf / beta_cdf, 2 ) );

  return variance;
}