Grokking Bayes. Quan Nguyen

A complete guide to thinking in Bayes, full of fun illustrations and friendly introductions.

Grokking Bayes introduces Bayesian statistics as a way of thinking and also a practical set of tools for making better decisions and predictions. Simple explanations, annotated visuals, and hands-on examples like tea vs. coffee preferences, predicting house prices, and testing medical treatments makes Bayesian statistics approachable–even if math isn’t your first language.

In Grokking Bayes you will discover how to:
 

• Move from priors and likelihoods to posteriors
• Inference with conjugate priors, MCMC, and variational inference
• Evaluate and compare models with posterior predictive checks, Bayes factors, and cross-validation
• Apply Bayesian methods to regression, mixture models, neural networks, decision-making, and experiment design

Bayesian statistics is a framework for reasoning under uncertainty. It lets you incorporate prior knowledge, rigorously quantify uncertainty, and directly answer practical questions like: “what’s the probability that this new treatment improves outcomes by at least 10%?” Bayesian methods are more intuitive, flexible, and directly actionable, which makes them invaluable for data science, AI, experiment design, and beyond.