bayesian-methods
Bayesian approach to statistical inference and decision-making. Covers prior specification, likelihood functions, posterior computation, conjugate priors, Markov chain Monte Carlo (MCMC), credible intervals, Bayesian hypothesis comparison (Bayes factors), hierarchical models, and the philosophical contrast with frequentist methods. Use when updating beliefs with data, specifying priors, computing posteriors, comparing Bayesian and frequentist approaches, or building hierarchical models.
npx skills add majiayu000/claude-skill-registry --skill bayesian-methods --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Bayesian Methods Bayesian statistics treats probability as a measure of belief, updated by data through Bayes' theorem. Where frequentist statistics asks "how likely are these data under H_0?", Bayesian statistics asks "given these data, what should I believe about the parameter?" This skill covers the machinery of Bayesian inference from prior specification through MCMC computation. **Agent affinity:** bayes (all Bayesian reasoning), efron (empirical Bayes, computational methods), box (model comparison) **Concept IDs:** stat-conditional-probability, stat-probability-foundations, stat-hypothesis-testing ## The Bayesian Framework ### Bayes' theorem for parameters P(theta | data) = P(data | theta) * P(theta) / P(data) - **P(theta):** Prior distribution. What you believe about theta before seeing data. - **P(data | theta):** Likelihood. The probability of the observed data given theta. - **P(theta | data):** Posterior distribution. Your updated belief about theta after seeing data. - **P(data):** Marginal likelihood (evidence). A normalizing constant: integral of P(data | theta) * P(theta) over all theta. In practice, the posterior is proportional to the likelihood times the prior:
- The Bayesian Framework
- Bayes' theorem for parameters
- Contrast with frequentist inference
- Prior Specification
- Types of priors
- Conjugate families
- Prior sensitivity analysis
- Posterior Computation
- Analytical posteriors (conjugate case)
- Markov chain Monte Carlo (MCMC)
- Credible Intervals
- Posterior credible intervals
- Types
- Bayesian Hypothesis Comparison
What does the bayesian-methods skill do?
Bayesian approach to statistical inference and decision-making. Covers prior specification, likelihood functions, posterior computation, conjugate priors, Markov chain Monte Carlo (MCMC), credible intervals, Bayesian hypothesis comparison (Bayes factors), hierarchical models, and the philosophical contrast with frequentist methods. Use when updating beliefs with data, specifying priors, computing posteriors, comparing Bayesian and frequentist approaches, or building hierarchical models.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill bayesian-methods --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.