Book Image

Bayesian Analysis with Python

Book Image

Bayesian Analysis with Python

Overview of this book

The purpose of this book is to teach the main concepts of Bayesian data analysis. We will learn how to effectively use PyMC3, a Python library for probabilistic programming, to perform Bayesian parameter estimation, to check models and validate them. This book begins presenting the key concepts of the Bayesian framework and the main advantages of this approach from a practical point of view. Moving on, we will explore the power and flexibility of generalized linear models and how to adapt them to a wide array of problems, including regression and classification. We will also look into mixture models and clustering data, and we will finish with advanced topics like non-parametrics models and Gaussian processes. With the help of Python and PyMC3 you will learn to implement, check and expand Bayesian models to solve data analysis problems.
Table of Contents (15 chapters)
Bayesian Analysis with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Summary


In this chapter, we learned about mixture models, a type of hybrid model useful to solve a large collection of problems. Creating a finite mixture model is a relatively easy task given what we have learned from previous chapters. A very handy application of this type of model is dealing with an excess of zeros in count data or for example to expand a Poisson model if we observe over-dispersion. Another application we explored was about extending logistic regression to handle outliers. We also briefly discussed the central elements of performing Bayesian (or model-based) clustering. Lastly, we presented a more theoretical discussion about continuous mixture models and how these types of models are connected to concepts we already learned in previous chapters, such as hierarchical models and the Student's t-distribution for robust models.