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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

6.11 Exercises

  1. Read the Bambi documentation ( https://bambinos.github.io/bambi/) and learn how to specify custom priors.

  2. Apply what you learned in the previous point and specify a HalfNormal prior for the slope of model_t.

  3. Define a model like model_poly4, but using raw polynomials, compare the coefficients and the mean fit of both models.

  4. Explain in your own words what a distributional model is.

  5. Expand model_spline to a distributional model. Use another spline to model the α parameter of the NegativeBinomial family.

  6. Create a model named model_p2 for the body_mass with the predictors bill_length, bill_depth, flipper_length, and species.

  7. Use LOO to compare the model in the previous point and model_p.

  8. Use the functions in the interpret module to interpret model_p2. Use both plots and tables.

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