pymc-bayesian-modeling

Name: pymc-bayesian-modeling
Rating: 5 (1 reviews)
Author: bg-szy

Bayesian modeling with PyMC 5: priors, likelihood, NUTS/ADVI sampling, diagnostics (R-hat, ESS), LOO/WAIC comparison, prediction. Hierarchical, logistic, GP variants; predictive checks.

1stars

Updated 18 days ago

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PyMC Bayesian Modeling

Overview

PyMC is a Python library for Bayesian statistical modeling and probabilistic programming. It provides an expressive syntax for defining probabilistic models and efficient inference via MCMC (NUTS) and variational methods (ADVI). This skill covers the full Bayesian modeling cycle from model specification through diagnostics, comparison, and prediction.

When to Use

Estimating parameters with full uncertainty quantification (credible intervals, not just

[Description truncada. Veja o README completo no GitHub.]

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