Book Image

Bayesian Analysis with Python - Second Edition

By : Osvaldo Martin
4.5 (2)
Book Image

Bayesian Analysis with Python - Second Edition

4.5 (2)
By: Osvaldo Martin

Overview of this book

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.
Table of Contents (11 chapters)
9
Where To Go Next?

Occam's razor – simplicity and accuracy

When choosing among alternatives, there is a guiding principle known as Occam's razor that loosely states the following:

If we have two or more equivalent explanations for the same phenomenon, we should choose the simpler one.

There are many justifications for this heuristic; one of them is related to the falsifiability criterion introduced by Popper. Another takes a pragmatic perspective and states that: Given simpler models are easier to understand than more complex models, we should keep the simpler one. Another justification is based on Bayesian statistics, as we will see when we discus Bayes factors. Without getting into the details of these justifications, we are going to accept this criterion as a useful rule of thumb for the moment, just something that sounds reasonable.

Another factor we generally should take into...