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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
Languages
Tools
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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

8.9 Regression with spatial autocorrelation

The following example is taken from Statistical Rethinking: A Bayesian Course with Examples in R and STAN, Second Edition by Richard McElreath, Copyright (2020) by Chapman and Hall/CRC. Reproduced by permission of Taylor & Francis Group. I strongly recommend reading this book, as you will find many good examples like this and very good explanations. The only caveat is that the book examples are in R/Stan, but don’t worry and keep sampling; you will find the Python/PyMC version of those examples in the https://github.com/pymc-devs/pymc-resources resources.

For this example we have 10 different island societies; for each one of them, we have the number of tools they use. Some theories predict that larger populations develop and sustain more tools than smaller populations. Thus, we have a regression problem where the dependent variable is the number of tools and the independent variable is the population. Because the number of tools...

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