Build and infer Bayesian models with PyMC using MCMC and variational inference.
npx skills add https://github.com/swaruplab/operon --skill pymc-swaruplab
Please help me install this Agent Skill. Skill: pymc Source: https://github.com/swaruplab/operon/tree/main/src-tauri/protocols/pymc Command: npx skills add https://github.com/swaruplab/operon --skill pymc-swaruplab
PyMC enables practitioners to build and infer probabilistic Bayesian models from data, enabling principled uncertainty quantification.
Define a simple PyMC model, run the sampler, and inspect the posterior.
High-intent search queries and answers about installing and using this skill.
Build hierarchical Bayesian models in PyMC by defining group-level priors and linking them to individual observations. This captures varying effects across groups and quantifies uncertainty in multilevel data structures using MCMC sampling.
Diagnose MCMC convergence and perform posterior predictive checks using ArviZ. It provides trace plots, R-hat statistics, and posterior predictive checks to validate Bayesian inference and ensure sampler reliability.
Yes, you can use variational inference for scalable Bayesian analysis as an alternative to MCMC. It approximates posterior distributions faster, making it suitable for larger datasets where traditional NUTS sampling is computationally expensive.
Compare Bayesian models using LOO or WAIC through ArviZ integration. These information criteria evaluate out-of-sample predictive accuracy, allowing you to select the best-fitting model among competing probabilistic frameworks.
Yes, you need NumPy and pandas to fit Bayesian models with PyMC. These standard scientific libraries handle data manipulation and array operations required for defining model likelihoods and preparing input data structures.
Avoid Bayesian inference when you need point estimates without uncertainty bounds, face severe computational constraints, or lack prior distribution information. Frequentist methods may be more efficient for simple, large-scale data analysis workflows.
danielsimonjrCompute posterior beliefs using Bayesian updating and Dempster-Shafer reasoning.
fuzzy-dynamicsBuild and infer Bayesian models with MCMC sampling and variational inference.
Lord1EgyptConstruct and validate Bayesian hierarchical models with PyMC and ArviZ.
ZurybrGuide MCMC sampling with RStan or PyStan for Bayesian models.
