Does JAGS support discrete parameters?

Yes, unlike Stan which requires marginalization, JAGS allows for direct sampling of discrete parameters using distributions like dbern, dbin, and dpois.

How do I handle precision in BUGS models?

In BUGS/JAGS, the second parameter of dnorm is precision (tau). To use standard deviation (sigma) in your model, you must define tau as pow(sigma, -2) within your model block.

Can this skill help me run JAGS from R?

Yes, the skill includes patterns for using R2jags, including data list preparation, initial value function definition, and parameter tracking for MCMC chains.

What is the main difference between BUGS and Stan syntax?

BUGS uses a declarative syntax where the order of statements does not matter, whereas Stan uses an imperative block-based approach. Most importantly, BUGS uses precision (1/variance) for normal distributions, while Stan uses standard deviation.

BUGS & JAGS Bayesian Modeling Fundamentals

Name: BUGS & JAGS Bayesian Modeling Fundamentals
Author: choxos

bychoxos

0•

Data Science & ML

Master the foundational syntax and precision parameterization required for BUGS and JAGS statistical modeling.

This skill equips Claude with specialized knowledge for developing and auditing Bayesian hierarchical models using BUGS and JAGS. It addresses the critical precision parameterization pitfall—where BUGS uses inverse variance instead of standard deviation—and provides a complete distribution reference for continuous, discrete, and multivariate data. Whether you are building new declarative models, converting Stan code to BUGS, or integrating scripts into R workflows via R2jags, this skill ensures mathematical accuracy and syntactic correctness in your Bayesian workflows.

Key Features

010 GitHub stars

02Expert guidance on precision parameterization (tau = 1/variance)

03Conversion logic for migrating between BUGS and Stan

04R integration templates for R2jags and R2WinBUGS

05Detailed distribution library including multivariate and discrete types

06Comprehensive declarative syntax reference for JAGS and WinBUGS

Use Cases

01Building and debugging hierarchical Bayesian models in JAGS

02Troubleshooting distribution parameterization and initialization errors

03Converting legacy WinBUGS code to modern R-integrated workflows

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add choxos/bayesianagent bugs-fundamentals

For use in Claude.ai and ChatGPT

Download Skill