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Bayesian Analysis with Python

You're reading from Bayesian Analysis with Python A practical guide to probabilistic modeling

Product type Paperback

Published in Jan 2024

Publisher Packt

ISBN-13 9781805127161

Length 394 pages

Edition 3rd Edition

Languages

Python

Tools

PyCharm

Concepts

Machine Learning

Author (1):

Osvaldo Martin

View More author details

Table of Contents (15) Chapters

Preface

1. Chapter 1 Thinking Probabilistically

2. Chapter 2 Programming Probabilistically FREE CHAPTER

3. Chapter 3 Hierarchical Models

4. Chapter 4 Modeling with Lines

5. Chapter 5 Comparing Models

6. Chapter 6 Modeling with Bambi

7. Chapter 7 Mixture Models

8. Chapter 8 Gaussian Processes

9. Chapter 9 Bayesian Additive Regression Trees

10. Chapter 10 Inference Engines

11. Chapter 11 Where to Go Next

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12. Bibliography

13. Other Books You May Enjoy

14. Index

Bibliography

Oriol Abril-Pla, Virgile Andreani, Colin Carroll, Larry Dong, Christopher J. Fonnesbeck, Maxim Kochurov, Ravin Kumar, Jupeng Lao, Christian C. Luhmann, Osvaldo A. Martin, Michael Osthege, Ricardo Vieira, Thomas Wiecki, and Robert Zinkov. Pymc: A modern and comprehensive probabilistic programming framework in python. PeerJ Computer Science, 9:e1516, 2023. doi: 10.7717/peerj-cs.1516.

Agustina Arroyuelo, Jorge A. Vila, and Osvaldo A. Martin. Exploring the quality of protein structural models from a bayesian perspective. Journal of Computational Chemistry, 42(21): 1466–1474, 2021. doi: https://doi.org/10.1002/jcc.26556. URL https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.26556.

Joseph K. Blitzstein. Introduction to Probability 2ed. Chapman and Hall/CRC, Boca Raton, 2 edition edition, February 2019. ISBN 978-1-138-36991-7.

Luis Jorge Borges. Ficciones. Sur...

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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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