Bayesian Statistics Guide

A skill for applying Bayesian statistical methods to research data analysis. Covers prior specification, Markov chain Monte Carlo (MCMC) sampling, posterior interpretation, model comparison, and reporting standards.

Bayesian Framework Overview

Bayes' Theorem in Practice

Posterior = (Likelihood x Prior) / Evidence

P(theta | data) = P(data | theta) * P(theta) / P(data)

In practice:
  P(theta | data) is proportional to P(data | theta) * P(theta)
  (the denominator is a normalizing constant)

When to Use Bayesian Methods