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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

2.10 Exercises

  1. Using PyMC, change the parameters of the prior Beta distribution in our_first_model to match those of the previous chapter. Compare the results to the previous chapter.

  2. Compare the model our_first_model with prior θ ∼ Beta(1,1) with a model with prior θ ∼(0,1). Are the posteriors similar or different? Is the sampling slower, faster, or the same? What about using a Uniform over a different interval such as [-1, 2]? Does the model run? What errors do you get?

  3. PyMC has a function pm.model_to_graphviz that can be used to visualize the model. Use it to visualize the model our_first_model. Compare the result with the Kruschke diagram. Use pm.model_to_graphviz to visualize model comparing_groups.

  4. Read about the coal mining disaster model that is part of the PyMC documentation ( https://shorturl.at/hyCX2). Try to implement and run this model by yourself.

  5. Modify model_g, change the prior for the mean to a Gaussian distribution centered at the...

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