BAYESIAN INFERENCE WITH PYTHON FOR BEGINNERS: Understanding the Fundamentals of Bayesian Analysis with Python

by Tyler Green

Bayesian Inference with Python for Understanding the Fundamentals of Bayesian Analysis with Python
Feeling overwhelmed by the complexity of statistical models and struggling to understand Bayesian Inference?
If you're finding it difficult to dive into the world of Bayesian statistics with Python, you're not alone.
Bayesian Inference with Python for Beginners offers a straightforward, step-by-step approach to learning one of the most powerful techniques in modern data science. Gone are the days of confusing jargon and overwhelming mathematical formulas, this book makes Bayesian Inference easy to understand and apply.
Written for beginners, data enthusiasts, and those new to Bayesian statistics, "Bayesian Inference with Python for Beginners" is your ultimate guide to mastering the essentials of Bayesian analysis through hands-on Python coding. This isn’t just a textbook; it's your toolkit for unlocking the full potential of probabilistic modeling.
Inside, you’ll find over practical examples and exercises to guide you through the concepts and get you coding confidently. Whether you're learning the basics or exploring into more advanced concepts, each chapter will give you the tools you need to apply Bayesian techniques to real-world problems.
Here's why this book is a
✔️ Beginner-Friendly: No prior experience with Bayesian statistics or Python required. Every chapter is written in clear, simple language and includes practical coding exercises to reinforce key concepts.
✔️ Hands-On Python Code: Learn Bayesian Inference by doing. Each chapter includes code snippets and exercises that allow you to experiment and apply the concepts immediately.
✔️ Code Examples for Real-World Problems: From basic probability to model building, this book includes Python code examples designed to solve real-world problems like regression, classification, and data analysis using Bayesian methods.
These are the valuable skills you will
Understanding Probability Theory: Learn how to apply basic probability concepts in the context of Bayesian statistics.
Bayesian Modeling with Python: Build your first Bayesian models using PyMC3 and other Python libraries.
MCMC Sampling & Variational Inference: Master MCMC sampling and learn about modern techniques like Variational Inference to efficiently estimate complex models.
Practical Data Analysis: Work with real data, applying Bayesian methods to make informed predictions and decisions.
Evaluating Model Performance: Learn how to assess the performance of your Bayesian models with diagnostic tools like trace plots and posterior predictive checks.
These are the key concepts you will
🔹 Introduction to Bayesian Probability: Grasp the core idea of updating beliefs with new data and understand how Bayes’ Theorem works.
🔹 Working with Priors and Likelihoods: Learn how to choose appropriate priors and compute likelihoods to update your models.
🔹 MCMC Sampling: Dive into Markov Chain Monte Carlo methods to sample from posterior distributions, a crucial part of advanced Bayesian modeling.
🔹 Model Evaluation and Diagnostics: Master tools for checking convergence, assessing model fit, and making sure your results are reliable.

170 pages, Kindle Edition

Published January 4, 2026

About the author

Tyler Green is an award-winning critic and historian. He is the author of the forthcoming "Carleton Watkins: Making the West American," which will be published by University of California Press in October, and the producer and host of The Modern Art Notes Podcast, America's most popular audio program on art.

Tyler Green is an award-winning critic and historian. He is the author of “Carleton Watkins: Making the West American,” which will be published by University of California Press in October, and the producer and host of The Modern Art Notes Podcast, America's most popular audio program on art.

Carleton Watkins (1829–1916) is widely considered the greatest American photographer of the nineteenth century and the most influential artist of his era. He is best known for his pictures of Yosemite Valley and the nearby Mariposa Grove of giant sequoias. “Watkins” tells the story of Watkins’s influence on the West, photography and art.

In 2014, the U.S. chapter of the International Association of Art Critics (AICA-USA) awarded Green one of its two inaugural awards for art criticism for his website Modern Art Notes. The award also included a citation for The MAN Podcast. (The other inaugural award was given to New York Times critic Holland Cotter.)

The Modern Art Notes Podcast is a weekly, interview program, a "Fresh Air" for art. Pulitzer Prize-winning art critic Sebastian Smee has called The MAN Podcast "one of the great archives of the art of our time." The BBC named the program one of the world's top 25 culture podcasts.

Since debuting in 2011, the show has aired over 360 weekly episodes. Guests have included artists Richard Serra, Robert Irwin, Sophie Calle, Julie Mehretu, Wayne Thiebaud, Thomas Struth, Kerry James Marshall, Frank Stella, Olafur Eliasson, Carrie Mae Weems, Mark Bradford, Chris Burden, Robert Adams, Shirin Neshat, and Barbara Kruger, historians such as Jonathan Brown and Sarah Lewis, and Pulitzer-winning authors/critics such as Smee, Mark Stevens and Paul Goldberger. Nearly twenty of America's most prominent art museums have advertised on the program, including the Museum of Modern Art, New York, SFMOMA, and the J. Paul Getty Museum.

Between 2001 and 2014, Green's pioneering Modern Art Notes website featured original reporting, art criticism, and analyses of both art and non-profit art institutions. Newspapers such as the New York Times, the Los Angeles Times and the Wall Street Journal all credited MAN with breaking stories that they later covered. The WSJ called Modern Art Notes "the most influential of all visual arts blogs," and later wrote, "You won't find a better-informed art writer than Tyler Green." MAN was the first website to feature original, digitally published art journalism and criticism.

Green has written for many print and digital magazines, including New York Times Lens, Fortune, Conde Nast Portfolio and Smithsonian. He also spent a year as Bloomberg's art critic. From 2010-2014 he was the columnist for Modern Painters magazine.

Green has contributed op-eds to newspapers such as the Los Angeles Times, the Boston Globe, the Philadelphia Inquirer and the WSJ. His commentary has also aired on National Public Radio's "All Things Considered." Books featuring his work include "San Francisco Museum of Modern Art 360: Views on the Collection," a forthcoming David Maisel monograph, and a 2018 Anne Appleby exhibition catalogue published by the Tacoma (Wash.) Art Museum.