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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

Chapter 2
Programming Probabilistically

Our golems rarely have a physical form, but they too are often made of clay living in silicon as computer code. – Richard McElreath

Now that we have a very basic understanding of probability theory and Bayesian statistics, we are going to learn how to build probabilistic models using computational tools. Specifically, we are going to learn about probabilistic programming with PyMC [Abril-Pla et al.2023]. The basic idea is that we use code to specify statistical models and then PyMC will solve those models for us. We will not need to write Bayes’ theorem in explicit form. This is a good strategy for two reasons. First, many models do not lead to an analytic closed form, and thus we can only solve those models using numerical techniques. Second, modern Bayesian statistics is mainly done by writing code. We will be able to see that probabilistic programming offers an effective way to build and solve complex models and...

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