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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

6.8 Interpreting models with Bambi

We have been using bmb.interpret_plot_predictions a lot in this chapter. But that’s not the only tool that Bambi offers us to help us understand models. One of them is bmb.interpret_plot_comparisons. This tool helps us answer the question, ”What is the expected predictive difference when we compare two values of a given variable while keeping all the rest at constant values?”.

Let’s use model_int from the previous section, so we don’t need to fit a new model. We use the following code block to generate Figure 6.15:

Code 6.20

bmb.interpret.plot_comparisons(model_int, idata_int, 
                               contrast={"bill_depth":[1.4, 1.8]}, 
                ...
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