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Grokking Bayes
Paperback
Publisher Price: $69.99
ISBN13: 9781633434516
Publisher: Manning Publications
Published: Dec 29 2026
Pages: 275
Weight: 0.72
Language: English
- 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.
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