Individual skill
bayesian-statistics
Bayesian data analysis — priors, likelihoods, posterior computation, model comparison, and honest reporting.
skills/bayesian-statistics/SKILL.md
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## Overview
Bayesian statistics treats parameters as random variables and updates
beliefs with data: posterior ∝ likelihood × prior. This skill covers
choosing priors that regularize without dictating, building likelihoods
that respect the data-generating process, computing posteriors (MCMC,
variational), checking models (posterior predictive checks), and
comparing models without fooling yourself.
## When to use
- Estimating parameters with proper uncertainty quantification
- Incorporating prior knowledge or handling small datasets where priors matter
- Hierarchical/multilevel modeling: partial pooling across groups