Pymc Bayesian Modeler

PyMC probabilistic programming skill for hierarchical Bayesian models in physics data analysis

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PyMC Bayesian Modeler

Purpose

Provides expert guidance on PyMC for Bayesian modeling in physics, including hierarchical models and advanced inference methods.

Capabilities

  • Probabilistic model construction
  • NUTS/HMC sampling
  • Variational inference
  • Gaussian processes
  • Model comparison (WAIC, LOO)
  • Prior predictive checks

Usage Guidelines

  1. Model Building: Construct probabilistic models
  2. Priors: Specify informative or weakly informative priors
  3. Sampling: Use NUTS for efficient sampling
  4. Diagnostics: Check convergence with trace plots and r-hat
  5. Comparison: Compare models with information criteria

Tools/Libraries

  • PyMC
  • arviz
  • Theano/JAX

a5c-ai/babysitter/tree/main/library/specializations/domains/science/physics/skills/pymc-bayesian-modeler commit 5d74bacfc0

Frequently asked questions

npx skillmds@latest add a5c-ai/pymc-bayesian-modeler