pymc-bayesian-modeler

Included with Lifetime

$97 forever

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

General

What this skill does


# 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

Files: 1

Size: 1.0 KB

Complexity: 8/100

Category: General

Source: https://github.com/a5c-ai/babysitter/tree/main/library/specializations/domains/science/physics/skills/pymc-bayesian-modeler

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