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Bayesian Analysis with Python

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

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781805127161
Length 394 pages
Edition 3rd Edition
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Author (1):
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Osvaldo Martin Osvaldo Martin
Author Profile Icon Osvaldo Martin
Osvaldo Martin
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Table of Contents (15) Chapters Close

Preface
1. Chapter 1 Thinking Probabilistically FREE CHAPTER 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 5
Comparing Models

A map is not the territory it represents, but, if correct, it has a similar structure to the territory. – Alfred Korzybski

Models should be designed as approximations to help us understand a particular problem or a class of related problems. Models are not designed to be verbatim copies of the real world. Thus, all models are wrong in the same sense that maps are not the territory. But not all models are equally wrong; some models will be better than others at describing a given problem.

In the previous chapters, we focused our attention on the inference problem, that is, how to learn the values of parameters from data. In this chapter, we are going to focus on a complementary problem: how to compare two or more models for the same data. As we will learn, this is both a central problem in data analysis and a tricky one. In this chapter, we are going to keep examples super simple, so we can focus on the technical aspects of model comparison. In...

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