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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 2. Chapter 2 Programming Probabilistically FREE CHAPTER 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

3.7 Exercises

  1. Using your own words explain the following concepts in two or three sentences:

    • Complete pooling

    • No pooling

    • Partial pooling

  2. Repeat the exercise we did with model_h. This time, without a hierarchical structure, use a flat prior such as Beta(α = 1,β = 1). Compare the results of both models.

  3. Create a hierarchical version of the tips example from Chapter 2, by partially pooling across the days of the week. Compare the results to those obtained without the hierarchical structure.

  4. For each subpanel in Figure 3.7, add a reference line representing the empirical mean value at each level, that is, the global mean, the forward mean, and Messi’s mean. Compare the empirical values to the posterior mean values. What do you observe?

  5. Amino acids are usually grouped into categories such as polar, non-polar, charged, and special. Build a hierarchical model similar to cs_h but including a group effect for the amino acid category. Compare the results to those...

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