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Bayesian Analysis with Python

You're reading from Bayesian Analysis with Python Unleash the power and flexibility of the Bayesian framework

Product type Paperback

Published in Nov 2016

Last Updated in Feb 2025

Publisher Packt

ISBN-13 9781785883804

Length 282 pages

Edition 1st Edition

Languages

Python

Concepts

Data Science

Table of Contents (10) Chapters

Preface

1. Thinking Probabilistically - A Bayesian Inference Primer

2. Programming Probabilistically – A PyMC3 Primer FREE CHAPTER

3. Juggling with Multi-Parametric and Hierarchical Models

4. Understanding and Predicting Data with Linear Regression Models

5. Classifying Outcomes with Logistic Regression

6. Model Comparison

7. Mixture Models

8. Gaussian Processes

Index

Keep reading

Gaussian Processes for Machine Learning by Carl Edward Rasmussen and Christopher K. I. Williams
Chapter 4 and 15 Machine Learning a Probabilistic Perspective by Kevin Murhpy
Chapter 11 Statistical Rethinking, Richard McElreath.
Chapter 22 Bayesian Data Analysis, Third Edition Gelman et al

The rest of the chapter is locked

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