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This book 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.

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.

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

About the book

Grokking Bayes teaches Bayesianism through clear explanations, rich illustrations, and relatable examples. You’ll first build an intuition, then translate that intuition into working code with Python tools like the PyMC library and ArviZ package. Along the way, you’ll explore how Bayesian ideas connect to modern AI, from uncertainty-aware deep learning to LLM applications.

Throughout, the book focuses on the skills for making better decisions: you’ll go from inference to Bayesian decision theory and even experimental design. Whether you’re a data scientist, AI practitioner, or curious learner, Grokking Bayes will give you the tools to make smarter decisions without needing to rely on long-run frequencies or reams of data, and without drowning you in probability theory and abstract math.

About the reader

All you need to start grokking Bayes is high school math and the basics of Python programming.

About the author

Quan Nguyen is a Python programmer and machine learning researcher with a focus on decision-making under uncertainty. He has authored several books on Python programming and scientific computing. Quan earned his PhD in computer science from Washington University in St. Louis, where his research centered on Bayesian methods in machine learning. He is currently a postdoctoral researcher at Princeton University.