bayesian-inference-engine

name	bayesian-inference-engine
description	Bayesian probabilistic reasoning for prior specification, posterior computation, and belief updating
allowed-tools	["Bash","Read","Write","Edit","Glob","Grep"]
metadata	{"specialization":"scientific-discovery","domain":"science","category":"hypothesis-reasoning","phase":6}
graph	{"domains":["domain:scientific-discovery"],"specializations":["specialization:scientific-research-methods"],"skillAreas":["skill-area:data-analysis","skill-area:statistical-analysis","skill-area:deep-web-research"],"workflows":["workflow:experiment-design","workflow:peer-review-cycle"],"roles":["role:research-engineer","role:computational-scientist"]}

Bayesian Inference Engine

Purpose

Provides Bayesian probabilistic reasoning capabilities for prior specification, posterior computation, and sequential belief updating.

Capabilities

• Prior elicitation support
• MCMC sampling (NUTS, HMC)
• Variational inference
• Model comparison (Bayes factors, LOO-CV)
• Posterior predictive checking
• Sequential belief updating

Usage Guidelines

1. Prior Selection: Choose appropriate, defensible priors
2. Sampling: Use efficient MCMC algorithms
3. Diagnostics: Check convergence and mixing
4. Model Comparison: Use appropriate comparison criteria

Tools/Libraries

• PyMC
• Stan (PyStan)
• ArviZ
• NumPyro