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Bayesian Analysis with Python

You're reading from Bayesian Analysis with Python Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ

Product type Paperback

Published in Dec 2018

Publisher Packt

ISBN-13 9781789341652

Length 356 pages

Edition 2nd Edition

Languages

Python

Concepts

Data Science

Author (1):

Osvaldo Martin

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Table of Contents (11) Chapters

Preface

1. Thinking Probabilistically

2. Programming Probabilistically FREE CHAPTER

3. Modeling with Linear Regression

4. Generalizing Linear Models

5. Model Comparison

6. Mixture Models

7. Gaussian Processes

8. Inference Engines

9. Where To Go Next?

10. Other Books You May Enjoy

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Who this book is for

If you are a student, data scientist, researcher in the natural or social sciences, or a developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory, so no previous statistical knowledge is required, although some experience in using Python and NumPy is expected.

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Authors (1)

Osvaldo Martin

Osvaldo Martin

Osvaldo Martin is a researcher at CONICET, in Argentina. He has experience using Markov Chain Monte Carlo methods to simulate molecules and perform Bayesian inference. He loves to use Python to solve data analysis problems. He is especially motivated by the development and implementation of software tools for Bayesian statistics and probabilistic modeling. He is an open-source developer, and he contributes to Python libraries like PyMC, ArviZ and Bambi among others. He is interested in all aspects of the Bayesian workflow, including numerical methods for inference, diagnosis of sampling, evaluation and criticism of models, comparison of models and presentation of results.

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