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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

9.1 Decision trees

Before jumping into BART models, let’s take a moment to discuss what decision trees are. A decision tree is like a flowchart that guides you through different questions until you reach a final choice. For instance, suppose you need to decide what type of shoes to wear every morning. To do so, you may ask yourself a series of questions. ”Is it warm?” If yes, you then ask something more specific, like ”Do I have to go to the office?” Eventually, you will stop asking questions and reach an output value like flip-flops, sneakers, boots, moccasins, etc.

This flowchart can be conveniently encoded in a tree structure, where at the root of the tree we place more general questions, then proceed along the tree to more and more specific ones, and finally arrive at the leaves of the tree with the output of the different types of shoes. Trees are very common data structures in computer science and data analysis.

More formally, we can say that...

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