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Bayesian Analysis with Python - Third Edition

You're reading from  Bayesian Analysis with Python - Third Edition

Product type Book
Published in Jan 2024
Publisher Packt
ISBN-13 9781805127161
Pages 394 pages
Edition 3rd Edition
Languages
Author (1):
Osvaldo Martin Osvaldo Martin
Profile icon Osvaldo Martin

Table of Contents (15) Chapters

Preface
1. Chapter 1 Thinking Probabilistically 2. Chapter 2 Programming Probabilistically 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 12. Bibliography
13. Other Books You May Enjoy
14. Index

10.1 Inference engines

While conceptually simple, Bayesian methods can be mathematically and numerically challenging. The main reason is that the marginal likelihood, the denominator in Bayes’ theorem, usually takes the form of an intractable or computationally expensive integral to solve. For this reason, the posterior is usually estimated numerically using algorithms from the Markov Chain Monte Carlo (MCMC) family. These methods are sometimes called inference engines, because, at least in principle, they are capable of approximating the posterior distribution for any probabilistic model. Even though inference does not always work that well in practice, the existence of such methods has motivated the development of probabilistic programming languages such as PyMC.

The goal of probabilistic programming languages is to separate the model-building process from the inference process to facilitate the iterative steps of model-building, evaluation, and model modification/expansion...

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