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?

Regularizing priors

Using informative and weakly informative priors is a way of introducing bias in a model and, if done properly, this can be a really good because bias prevents overfitting and thus contributes to models being able to make predictions that generalize well. This idea of adding a bias to reduce a generalization error without affecting the ability of the model to adequately model the data that's used to fit is known as regularization. This regularization often takes the form of penalizing larger values for the parameters in a model. This is a way of reducing the information that a model is able to represent and thus reduces the chances that a model captures the noise instead of the signal.

The regularization idea is so powerful and useful that it has been discovered several times, including outside the Bayesian framework. In some fields, this idea is known...