bayesian-modeling
By choxos
Create, review, and validate Bayesian models in Stan, PyMC, JAGS, and WinBUGS
npx claudepluginhub choxos/biostatagent --plugin bayesian-modelingComponent Overview
Component Details
Commands (3)
Interactive workflow for creating Bayesian models in Stan, JAGS, WinBUGS, or PyMC
Review and improve existing Bayesian models for correctness, efficiency, and best practices
Execute Bayesian models with test data and report convergence diagnostics
Agents (6)
Expert in WinBUGS and JAGS model specification. Understands precision parameterization, d-prefix distributions, declarative syntax, and R integration via R2WinBUGS and R2jags packages.
Orchestrates Bayesian model creation and review. Routes to specialized agents based on user needs, language choice (Stan/JAGS/WinBUGS/PyMC), and model type. Entry point for all modeling tasks.
Reviews and validates Bayesian model specifications for correctness, efficiency, and best practices. Identifies syntax errors, missing priors, parameterization issues, and suggests improvements.
Expert in PyMC 5 for Bayesian modeling in Python. Creates and debugs PyMC models using modern syntax, understands all distribution types, sampling methods, and ArviZ diagnostics integration.
Expert in Stan 2.37 programming language for Bayesian inference. Creates and debugs Stan models using cmdstanr, understands all 7 program blocks, HMC/NUTS optimization, and modern Stan syntax.
Executes Stan, JAGS, WinBUGS, and PyMC models with test data to validate syntax and sampling. Generates synthetic data, runs short MCMC chains, and reports convergence diagnostics.
Skills (9)
Foundational knowledge for writing BUGS/JAGS models including precision parameterization, declarative syntax, distributions, and R integration. Use when creating or reviewing BUGS/JAGS models.
Patterns for hierarchical/multilevel Bayesian models including random effects, partial pooling, and centered vs non-centered parameterizations.
Bayesian meta-analysis models including fixed effects, random effects, and network meta-analysis with Stan and JAGS implementations.
MCMC diagnostics for Bayesian models including convergence assessment, effective sample size, divergences, and posterior predictive checks.
Foundational knowledge for writing PyMC 5 models including syntax, distributions, sampling, and ArviZ diagnostics. Use when creating or reviewing PyMC models.
Bayesian regression models including linear, logistic, Poisson, negative binomial, and robust regression with Stan and JAGS implementations.
Foundational knowledge for writing Stan 2.37 models including program structure, type system, distributions, and best practices. Use when creating or reviewing Stan models.
Bayesian survival analysis models including exponential, Weibull, log-normal, and piecewise exponential hazard models with censoring support.
Bayesian time series models including AR, MA, ARMA, state-space models, and dynamic linear models in Stan and JAGS.
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