Simulate Stochastic Process
Skill Verified ActiveSimulate stochastic processes (Markov chains, random walks, SDEs, MCMC) with convergence diagnostics, variance reduction, and visualization. Use when generating sample paths for estimation, prediction, or visualization; when analytical solutions are intractable; running Monte Carlo estimation needing convergence guarantees; validating analytical results against empirical simulation; or sampling from complex posteriors via MCMC.
To enable users to simulate and analyze complex stochastic processes when analytical solutions are intractable, ensuring convergence and providing empirical validation.
Features
- Simulate Markov chains (DTMC, CTMC)
- Simulate random walks and Brownian motion
- Simulate stochastic differential equations (SDEs)
- Simulate Markov Chain Monte Carlo (MCMC) samplers
- Provide convergence diagnostics (R-hat, ESS, Geweke)
- Apply variance reduction techniques
- Visualize trajectories and distributions
Use Cases
- Generating sample paths for estimation, prediction, or visualization
- Running Monte Carlo estimation with convergence guarantees
- Validating analytical results against empirical simulation
- Sampling from complex posterior distributions via MCMC
Non-Goals
- Providing analytical solutions for stochastic processes
- Performing real-time, low-latency simulations beyond typical analysis workflows
- Automating the theoretical derivation of SDE coefficients or MCMC proposal mechanisms
Practical Utility
- info:Usage examplesWhile the skill describes inputs and procedures, explicit end-to-end ready-to-use examples with invocation and output are not provided in the SKILL.md.
Installation
/plugin install agent-almanac@pjt222-agent-almanacQuality Score
VerifiedTrust Signals
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