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Bayesian Analysis with Python

You're reading from   Bayesian Analysis with Python Unleash the power and flexibility of the Bayesian framework

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Product type Paperback
Published in Nov 2016
Last Updated in Feb 2025
Publisher Packt
ISBN-13 9781785883804
Length 282 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (10) Chapters Close

Preface 1. Thinking Probabilistically - A Bayesian Inference Primer 2. Programming Probabilistically – A PyMC3 Primer FREE CHAPTER 3. Juggling with Multi-Parametric and Hierarchical Models 4. Understanding and Predicting Data with Linear Regression Models 5. Classifying Outcomes with Logistic Regression 6. Model Comparison 7. Mixture Models 8. Gaussian Processes Index

Exercises


  1. For the first example, modify the synthetic data to make it harder for the model to recover the true parameters; try increasing the overlap of the 3 Gaussians by changing the means and standard deviations. Try changing the number of points per cluster, and think of ways to improve the model for the harder data you come up with.

  2. Using the fish data, extend the model in the book to include the persons variable as part of a linear model. Include this variable to model the number of extra zeros. You should get a model with two linear models, one connecting the number of children and the presence/absence of a camper to the Poisson rate (as in the example we saw) and another connecting the number of persons to the variable. For the second case you will need a logistic inverse link!
  3. Use the data for the robust logistic example to feed a non-robust logistic regression model and to check that the outliers actually affected the results. You may want to add or remove outliers to better understand...

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