Bayesian Analysis with Python: A practical guide to probabilistic modeling

I’m still working on this one. I think it can be very powerful once I grasp all the math and coding better. It’s very interesting, well I am interested in math and solving things. Cool if you like this type of subject

This text is written for the intermediate Python developer with no experience in Bayesian Analysis. It starts at the beginning and gives the reader the foundation needed to start using these methods. The reader will learn to use multiple methods on top of an understanding of when to use them over others. The final instructive chapter covers the advanced topic of creating an inference engine. The book is well organized and easy to follow. I highly recommend this for developers of all kinds.

"Bayesian Analysis with Python" is a book that presents a modern and practical approach for mastering Bayesian statistical modeling using PyMC and other essential libraries. The book offers step-by-step guidance, including various concepts such as Bayesian additive regression trees (BART), and updated examples for enhanced understanding. It provides readers with a comprehensive understanding of probabilistic modeling, covering hierarchical models, generalized linear models, and Gaussian processes. This book offers practical insights for real-world applications, and it is a must-read for both novice and experienced practitioners.

This book provides an introduction to using Python for Bayesian Analysis, focusing on applying existing Python packages rather than teaching Bayesian statistics or Python itself. It assumes prior knowledge of Python and standard packages like NumPy. The writing style is informal and conversational, which I enjoyed, but some may not. The low-resolution, grayscale figures can be hard to read. Despite claiming that no prior statistical knowledge is needed, some background in probability and statistics would be beneficial as the first chapter is fast-paced. From chapter two onwards, practical Python code is well integrated, introducing the PyMC and ArviZ packages and demonstrating their use in various statistical models. The hands-on approach with easy-to-follow code snippets helps in understanding probabilistic modeling in Python. Overall, it's a valuable resource for beginners interested in Bayesian Analysis using Python.

If you had to buy just one book on Bayesian analysis, this is the one to get. It takes a lot of skill to write a concise, readable book on such a complicated topic.

good

While Python is my go-to language for things like NLP, I usually use R for everything else. After spending a solid long weekend with Martin's new book "Bayesian Analysis with Python" I can confirm that this book will be just what ONE audience needs, but may disappoint others. As a gentle introduction to Bayesian approaches for people who are well versed in intro statistics and have a solid foundation in Python, it's perfect. But if you're missing that mathematical statistics background (or if you're rusty on Python) this book may present a struggle. As a result, this is five stars for the target audience and four for the other audiences. The writing is clear and easy to follow, but sometimes encourages you to "review the code for understanding" where the text could have explained each of the lines of code in sequence. The book also assumes that the reader has a fundamental understanding of distributions and mathematical notation, which may not be the case for all programmers or data analysts. As a professor this would have been a great book to use from an introductory Bayesian methods course for juniors or seniors in STEM with at least one or two semesters of Python. For this group, the book is particularly strong, because it takes a computation-first approach but fills in the gaps with just enough theory. Highlights include: - There is a simple discussion on ROPE and loss functions that is valuable - There is a good discussion about how to do linear regression the Bayesian way (hint: all parameters treated as priors) - Some interesting mixture models using the Palmer Penguins dataset - The best part was the MCMC with Metropolis-Hastings to calculate the value of pi DO buy this book if you have a solid foundation in Python (and a Python environment already set up) and want to spend a few weeks (or a couple months) expanding your understanding into building and running simple Bayesian models. If you have the time to spend, this will deepen your understanding. DO NOT buy this book if you are a programmer who needs to start building Bayesian models at work within the next couple days! It's not going to help you work that next ticket in the queue.

あまり買う意味がない気がします。パラ読みした感じ、公式チュートリアルなどでほぼカバーされてる気がします。

Perfect

You know those foggy days where everything is confused? It seems that everything is the same. Then, one fine day, the wind comes and everything becomes clearer. The book " Bayesian Analysis with Python - Third Edition: A practical guide to probabilistic modeling" by Osvaldo Martin is this breath of clarity. The idea of putting only the essential Python code in the book and sending the complete code back to github is also very nice. Everything is much clearer and brighter. Thank you Osvaldo.

Excellent introduction to Bayesian analysis using Python! You can think of this book as a succinct user's guide for people who want to apply Bayesian analysis in their own projects. Updated code included in the book leverage recent versions of PyMC, Bambi and Arviz that have changed quite extensively since the second edition of the book. I have no hesitation in recommending this book. Osvaldo is a well-respected leader in the PyMC community and this book benefits from improvements based on feedback from the previous two editions. The price of this book has reduced quite a lot since I purchased it so if you are reading this while the price is around A$70 then don't hesitate because it is a bargain at that price. It easily compares with Bayes Rules! that provides an introduction to Bayesian methods using R.

This is a really great book to get started with building your own models in PyMC and I highly recommend it. I enjoyed the build-first approach and the accompanying Jupyter notebooks (which had to be adjusted in places to reflect how quickly the libraries develop - it turns out that was also a good way to get familiar with the APIs). It would have been a five star rating if the chapters on the Dirichlet and Gaussian processes would have been elaborated a little bit more. Perhaps that’s something for the next version?