Thu Oct  6 19:48:48 2022

ornstein_uhlenbeck_test():
  Python version: 3.6.9
  Test ornstein_uhlenbeck.

ornstein_uhlenbeck_euler_test():
  Estimate a solution to the Ornstein-Uhlenbeck equation
  using the Euler method for stochastic differential equations.

  Using decay rate THETA =  2.0
  Using mean MU =  1.0
  Using variance SIGMA =  0.15
  Using initial value X0 =  2.0
  Using final time TMAX =  3.0
  Using number of timesteps N =  10000

ornstein_uhlenbeck_euler():
  Use an Euler method to approximate the solution of
  the Ornstein-Uhlenbeck stochastic differential equation:

    d x(t) = theta * ( mu - x(t) ) dt + sigma dW

  with initial condition x(0) = x0.
  Graphics saved as "ornstein_uhlenbeck_euler.png"

ornstein_uhlenbeck_euler_maruyama_test():
  Estimate a solution to the Ornstein-Uhlenbeck equation
  using the Euler-Maruyama method for stochastic 
  differential equations.

  Using decay rate THETA =  2.0
  Using mean MU =  1.0
  Using variance SIGMA =  0.15
  Using initial value X0 =  2.0
  Using final time TMAX =  3.0
  Using number of large timesteps N =  10000
  Using R =  16  small time steps per one large time step

ornstein_uhlenbeck_euler_maruyama():
  Use an Euler-Maruyama method to approximate the solution of
  the Ornstein-Uhlenbeck stochastic differential equation:

    d x(t) = theta * ( mu - x(t) ) dt + sigma dW

  with initial condition x(0) = x0.
  Graphics saved as "ornstein_uhlenbeck_maruyama.png"

ornstein_uhlenbeck_test():
  Normal end of execution.
Thu Oct  6 19:48:49 2022