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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 9
Bayesian Additive Regression Trees

Individually, we are one drop. Together, we are an ocean. – Ryunosuke Satoro

In the last chapter, we discussed the Gaussian process (GPs), a non-parametric model for regression. In this chapter, we will learn about another non-parametric model for regression known as Bayesian additive regression trees, or BART to friends. We can consider BART from many different perspectives. It can be an ensemble of decision trees, each with a distinct role and contribution to the overall understanding of the data. These trees, guided by Bayesian priors, work harmoniously to capture the nuances of the data, avoiding the pitfall of individual overfitting. Usually, BART is discussed as a standalone model, and software that implements it is usually limited to one or a few models. In this chapter, we will take a different approach and use PyMC-BART, a Python library that allows the use of BART models within PyMC.

In this chapter, we will cover the...

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