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Programming Probabilistically

"Our golems rarely have a physical form, but they too are often made of clay living in silicon as computer code."

- Richard McElreath

Now that we have a basic understanding of Bayesian statistics, we are going to learn how to build probabilistic models using computational tools. Specifically, we are going to learn about probabilistic programming with PyMC3. The basic idea is to specify models using code and then solve them in a more or less automatic way. It is not that we are too lazy to learn the mathematical way, nor are we elitist-hardcore-hackers-in-code. One important reason behind this choice is that many models do not lead to an analytic closed form, and thus we can only solve those models using numerical techniques.

Another reason to learn probabilistic programming is that modern Bayesian statistics is mainly done by writing code...

Key benefits

A step-by-step guide to conduct Bayesian data analyses using PyMC3 and ArviZ

A modern, practical and computational approach to Bayesian statistical modeling

A tutorial for Bayesian analysis and best practices with the help of sample problems and practice exercises.

Description

The second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models. The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others. By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to.

Who is this book for?

If you are a student, data scientist, researcher, or a developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory so no previous statistical knowledge is required, although some experience in using Python and NumPy is expected.

What you will learn

Build probabilistic models using the Python library PyMC3

Analyze probabilistic models with the help of ArviZ

Acquire the skills required to sanity check models and modify them if necessary

Understand the advantages and caveats of hierarchical models

Find out how different models can be used to answer different data analysis questions

Compare models and choose between alternative ones

Discover how different models are unified from a probabilistic perspective

Think probabilistically and benefit from the flexibility of the Bayesian framework

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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F. Salvi May 26, 2019

Super Book . Really well written and with interesting examples on the associated github website.

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Daniel C. Thomas Feb 07, 2021

I purchased my copy through packt directly, not through amazon.Wanted to write a positive review because I saw a lot of the negative reviews here that didn't seem fair. This book is really great and the author - and the pymc folks in general - are really helpful and responsive. The errors in typesetting and graph colors, etc, mentioned by others a bit overblown (at least, in my version), and they are almost certainly due to the publishing platform. Packt is really good for fast, hot-topic publication, but they are not famous for typo-free books, etc. I think many of the ones listed by other reviews have already been corrected, and the typos I found were minor. They did not subtract from my ability to learn from this book - I learned a lot, really quickly. If the typos really bother you, you can always run the code on the jupyter notebook, the author is still keeping these up-to-date.Every example I followed (most in the book) worked, especially when I checked my code against the notebooks. I did have to use an earlier (3.7) version of python, which has more to do with where pymc3 is right now - pymc3 didn't play well with my python 3.8 installation.I really wanted to get out of R for my introduction to bayesian methods, and keep things in the scipy ecosystem. This was the only book that was up-to-date and that I found approachable for this. I hope the typos in the book are corrected, but it doesn't really matter, I still find this the best quick-and-dirty intro. And I hope the author writes more books.

The book is really well written. The examples are accurate and interesting. The author has immediately answered a couple of my question. I definitely recommend it to learn bayesian analysis wit python.

J. Lowry Dec 20, 2021

Excellent

Bayesian Analysis with Python: Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ , Second Edition

What do you get with Print?

Bayesian Analysis with Python

Programming Probabilistically

Probabilistic programming

PyMC3 primer

Summarizing the posterior

Gaussians all the way down

Groups comparison

Hierarchical models

Summary

Exercises

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