Simulate Stochastic Process

Simulate sample paths from stochastic processes -- including discrete Markov chains, continuous-time processes, stochastic differential equations, and MCMC samplers -- with convergence diagnostics, variance reduction techniques, and trajectory visualization.

When to Use

• You need to generate sample paths from a stochastic process for estimation, prediction, or visualization
• Analytical solutions are intractable and simulation is the only feasible approach
• You are running Monte Carlo estimation and need convergence guarantees and uncertainty quantification
• You want to validate analytical results (stationary distributions, hitting times) against empirical simulation
• You need to sample from a complex posterior distribution using MCMC
• You are prototyping a stochastic model before committing to full analytical treatment

Inputs

Required