Perform Bayesian modeling with PyMC and ArviZ for MCMC sampling and diagnostics.
npx skills add https://github.com/nferrer-dev/claude-dotfiles --skill pymc-nferrer-dev
Please help me install this Agent Skill. Skill: pymc Source: https://github.com/nferrer-dev/claude-dotfiles/tree/main/skills/pymc Command: npx skills add https://github.com/nferrer-dev/claude-dotfiles --skill pymc-nferrer-dev
This skill addresses the complexity of Bayesian statistical modeling, providing a structured, reproducible framework for probabilistic programming that helps users avoid common pitfalls like convergence issues and model misspecification.
Use the pymc skill to build and fit a Bayesian linear regression model on the provided dataset and generate a diagnostic report.
High-intent search queries and answers about installing and using this skill.
Bayesian modeling uses prior predictive checks, MCMC sampling, and posterior validation to quantify uncertainty. This pipeline structures model building, fits complex probabilistic models, and generates automated diagnostic reports for rigorous validation.
Bayesian model comparison evaluates predictive accuracy using LOO and WAIC metrics. This workflow provides automated utilities for calculating information criteria, checking model reliability, and generating diagnostic reports to validate your selections.
PyMC supports hierarchical model construction for grouped data, robust linear and logistic regression, and time-series forecasting. It enables MCMC sampling and variational inference to fit complex probabilistic models with full uncertainty quantification.
MCMC sampling and variational inference fit Bayesian models to data. This process uses PyMC for probabilistic programming and ArviZ for automated diagnostic reporting, ensuring convergence and validating posterior distributions for complex data analysis.
Bayesian convergence issues often stem from model misspecification. This framework provides a reproducible pipeline with prior predictive checks and automated diagnostic reporting to help avoid common pitfalls and ensure valid posterior distributions.
Yes, the workflow integrates pandas for data preparation and ArviZ for diagnostic reporting. It uses these dependencies alongside PyMC and NumPy to execute prior predictive validation, MCMC sampling, and posterior analysis.
StarlitnightlyTransform raw tabular data into cleaned, structured datasets using pandas and numpy.
fuzzy-dynamicsBuild and infer Bayesian models with MCMC sampling and variational inference.
eyadsibaiBuild Bayesian models and run MCMC sampling with PyMC.
jimmc414Build, fit, and validate Bayesian models with PyMC.
