Tue May 20 22:21:48 2025 ornstein_uhlenbeck_test(): python version: 3.10.12 numpy version: 1.26.4 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 the 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 the 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. Tue May 20 22:21:49 2025