Stan probabilistic programming for Bayesian inference
Scanned 9/2/2026
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---
name: stan-bayesian-modeling
description: Stan probabilistic programming for Bayesian inference
allowed-tools:
- Bash
- Read
- Write
- Edit
- Glob
- Grep
metadata:
specialization: mathematics
domain: science
category: statistical-computing
phase: 6
graph:
domains: [domain:mathematics]
specializations: [specialization:computational-mathematics]
skillAreas: [skill-area:statistical-analysis, skill-area:mathematical-reasoning, skill-area:data-analysis]
workflows: [workflow:experiment-design]
roles: [role:research-scientist, role:data-scientist]
---
# Stan Bayesian Modeling
## Purpose
Provides Stan probabilistic programming capabilities for Bayesian inference and statistical modeling.
## Capabilities
- Stan model specification
- MCMC sampling (NUTS, HMC)
- Variational inference
- Prior predictive checks
- Posterior predictive checks
- Model comparison (LOO-CV, WAIC)
## Usage Guidelines
1. **Model Specification**: Write Stan code with clear blocks
2. **Prior Selection**: Choose appropriate, weakly informative priors
3. **Diagnostics**: Check Rhat, ESS, and divergences
4. **Model Comparison**: Use LOO-CV for model selection
## Tools/Libraries
- Stan
- CmdStan
- RStan
- PyStan
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