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

Model-based clustering


Clustering is part of the unsupervised family of statistical/machine learning tasks and is similar to classification, but a little bit more difficult since we do not know the correct labels!

If we do not know the correct labels we can try grouping data points together. Loosely speaking, points that are closer between themselves, under some metric, are defined as belonging to the same group and separated from the other groups. Clustering has many, many applications; for example, phylogenetics, a branch of biology studying the evolutionary relationships among biological entities, can be framed as clustering techniques applied to and guided by an evolutionary question. A more capitalist-driven application of clustering is determining which movie/book/song/you-name-the-product we may be interested in. We can try to guess this based on our consumption-record and how this record clusters with those of other users. As with other unsupervised learning tasks, we may be interested...