Simulate Stochastic Process
Skill Verifiziert AktivSimulate 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.
Funktionen
- 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
Anwendungsfälle
- 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
Nicht-Ziele
- 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-almanacQualitätspunktzahl
VerifiziertVertrauenssignale
Ähnliche Erweiterungen
PyMC Bayesian Modeling
99Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
Analyze Diffusion Dynamics
99Analyze the dynamics of diffusion processes using stochastic differential equations, Fokker-Planck equations, first-passage time distributions, and parameter sensitivity analysis. Use when deriving probability density evolution for a continuous-time diffusion process, computing mean first-passage times for bounded diffusion, analyzing how drift and diffusion parameters affect process behavior, or validating closed-form solutions against stochastic simulation.
Alterlab Pymc
98Part of the AlterLab Academic Skills suite. Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
Oraclaw Simulate
98Monte-Carlo-Simulation für KI-Agenten. Führen Sie Tausende von probabilistischen Szenarien aus, um Risiken zu modellieren, Umsätze zu prognostizieren, Projektzeitpläne abzuschätzen und Unsicherheiten zu quantifizieren. Unterstützt 6 Verteilungstypen.
Simulate Cpu Architecture
100Design and simulate a minimal CPU from scratch: define an instruction set architecture (ISA), build the datapath (ALU, register file, program counter, memory interface), design the control unit (hardwired or microprogrammed), implement the fetch-decode-execute cycle, and verify by tracing a small program clock cycle by clock cycle. The capstone "computer inside a computer" exercise that composes combinational and sequential building blocks into a complete processor.
Fit Drift Diffusion Model
100Fit cognitive drift-diffusion models (Ratcliff DDM) to reaction time and accuracy data with parameter estimation (drift rate, boundary separation, non-decision time), model comparison, and parameter recovery validation. Use when modeling binary decision-making with reaction time data, estimating cognitive parameters from experimental data, comparing sequential sampling model variants, or decomposing speed-accuracy tradeoff effects into latent cognitive components.