Build and infer Bayesian models with MCMC sampling and variational inference.
npx skills add https://github.com/fuzzy-dynamics/strings --skill pymc-fuzzy-dynamics
Please help me install this Agent Skill. Skill: pymc Source: https://github.com/fuzzy-dynamics/strings/tree/main/packages/skills/pymc Command: npx skills add https://github.com/fuzzy-dynamics/strings --skill pymc-fuzzy-dynamics
PyMC provides an end-to-end framework to design, fit, and diagnose Bayesian models in Python, enabling principled uncertainty quantification and robust decision making.
Train a simple Bayesian linear regression on your data with PyMC and inspect the posterior summaries.
High-intent search queries and answers about installing and using this skill.
You can build hierarchical Bayesian models by defining probabilistic layers in Python, enabling principled uncertainty quantification across grouped data structures. This approach supports robust decision making in applied analytics scenarios.
The best way to compare competing Bayesian models is by calculating Leave-One-Out (LOO) and WAIC criteria to evaluate predictive accuracy. This allows you to select the most robust model for your data.
You can run MCMC sampling (NUTS) and variational inference to estimate posterior distributions during probabilistic programming. These inference methods allow you to fit linear, logistic, and time-series models efficiently.
Yes, you can perform posterior predictive checks and visualize diagnostics with ArviZ to validate model fit. This integration ensures a reproducible workflow for analyzing Bayesian inference outputs.
You need Bayesian modeling for clinical trials when evaluating treatment effects requires rigorous uncertainty quantification. It allows you to infer posterior distributions and make robust decisions based on probabilistic outcomes.
jaechang-hitsPerform Bayesian modeling workflows with PyMC and ArviZ.
jg-youGenerate ArviZ diagnostic and substantive figures for Bayesian analyses.
SologaBuild and validate Bayesian models with PyMC using MCMC and variational inference.
RamanEbrahimiConstructs and analyzes Bayesian models using PyMC.
