truncated_normal_sparse_grid
    
    
    
      truncated_normal_sparse_grid,
      a MATLAB code which 
      computes a sparse grid based on a normal probability density function
      (PDF), also called a Gaussian distribution, that has been
      truncated to [A,+oo), (-oo,B] or [A,B].
    
    
      Licensing:
    
    
      The information on this web page is distributed under the MIT license.
    
    
      Languages:
    
    
      truncated_normal_sparse_grid is available in
      a MATLAB version and
      an Octave version.
    
    
      Related Data and Programs:
    
    
      
      truncated_normal_sparse_grid_test
    
    
      
      sparse_grid_hw,
      a MATLAB code which
      creates sparse grids based on Gauss-Legendre, Gauss-Hermite,
      Gauss-Patterson, or a nested variation of Gauss-Hermite rules,
      by Florian Heiss and Viktor Winschel.
    
    
      
      truncated_normal,
      a MATLAB code which 
      works with the truncated normal distribution over [A,B], or
      [A,+oo) or (-oo,B], returning the probability density function (PDF),
      the cumulative density function (CDF), the inverse CDF, the mean,
      the variance, and sample values.
    
    
      
      truncated_normal_rule,
      a MATLAB code which 
      computes a quadrature rule for a normal probability density function
      (PDF), also called a Gaussian distribution, that has been
      truncated to [A,+oo), (-oo,B] or [A,B].
    
    
      Author:
    
    
      Original MATLAB version by Florian Heiss and Viktor Winschel.
      This version by John Burkardt.
    
    
      Reference:
    
    
      
        - 
          Gene Golub, John Welsch,
          Calculation of Gaussian Quadrature Rules,
          Mathematics of Computation,
          Volume 23, Number 106, April 1969, pages 221-230.
         
        - 
          Florian Heiss, Viktor Winschel,
          Likelihood approximation by numerical integration on sparse grids,
          Journal of Econometrics,
          Volume 144, May 2008, pages 62-80.
         
        - 
          Norman Johnson, Samuel Kotz, Narayanaswamy Balakrishnan,
          Continuous Univariate Distributions,
          Second edition,
          Wiley, 1994,
          ISBN: 0471584940,
          LC: QA273.6.J6.
         
      
    
    
      Source Code:
    
    
      
        - 
          get_seq.m 
          generates integer vectors that describe component tensor products.
        
 
        - 
          i4vec_print.m 
          prints an I4VEC.
        
 
        - 
          nwspgr.m 
          the main routine, which the user calls in order to compute
          a particular sparse grid.
        
 
        - 
          nwspgr_size.m 
          returns the size of a particular sparse grid.
        
 
        - 
          quad_rule_print.m 
          prints a multidimensional quadrature rule.
        
 
        - 
          r8mat_write.m 
          writes an R8MAT to a file.
        
 
        - 
          r8vec_write.m 
          writes an R8VEC to a file.
        
 
        - 
          r8vec2_print.m 
          prints an R8VEC2.
        
 
        - 
          tensor_product.m 
          computes a tensor product quadrature rule.
        
 
        - 
          tno_order.m 
          returns the order (number of points) in a 
          Truncated Normal Odd (TNO) growth quadrature rule of given level.
        
 
        - 
          tn.m 
          returns the points and weights in a
          Truncated Normal quadrature rule
          of given order (number of points).
        
 
        - 
          tno.m 
          returns the points and weights in a 
          Truncated Normal Odd (TNO) growth quadrature rule
          of given level.
        
 
        - 
          tno_order.m 
          returns the number of points in a 
          Truncated Normal Odd (TNO) growth quadrature rule
          of given level.
        
 
        - 
          tno_sparse_grid_write.m 
          writes a TNO sparse grid to X and W files.
        
 
      
    
    
    
      Last revised on 11 April 2019.