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