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Bayesian Analysis with Python - Third Edition

You're reading from  Bayesian Analysis with Python - Third Edition

Product type Book
Published in Jan 2024
Publisher Packt
ISBN-13 9781805127161
Pages 394 pages
Edition 3rd Edition
Languages
Author (1):
Osvaldo Martin Osvaldo Martin
Profile icon Osvaldo Martin

Table of Contents (15) Chapters

Preface
1. Chapter 1 Thinking Probabilistically 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

4.11 Exercises

  1. Using the howell dataset (available at https://github.com/aloctavodia/BAP3), create a linear model of the weight (x) against the height (y). Exclude subjects that are younger than 18. Explain the results.

  2. For four subjects, we get the weights (45.73, 65.8, 54.2, 32.59), but not their heights. Using the model from the previous exercise, predict the height for each subject, together with their 50% and 94% HDIs. Tip: Use pm.MutableData.

  3. Repeat exercise 1, this time including those below 18 years old. Explain the results.

  4. It is known for many species that weight does not scale with height, but with the logarithm of the weight. Use this information to fit the howell data (including subjects from all ages).

  5. See the accompanying code model_t2 (and the data associated with it). Experiment with priors for ν, like the non-shifted Exponential and Gamma priors (they are commented on in the code). Plot the prior distribution to ensure that you understand them. An easy...

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