Bayesian statistics for confused data scientists
Summary
This article provides a beginner-friendly yet thorough tour of Bayesian statistics, contrasting it with frequentist approaches using intuitive die-roll examples. It covers core concepts (priors, likelihood, posteriors, credible intervals), practical computation with PyMC, and common issues like the impact of priors and handling outliers, supplemented by code snippets and applications to regression and data generation.