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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

10.11 Keep calm and keep trying

What should we do when diagnostics show problems? We should try to fix them. Sometimes, PyMC will provide suggestions on what to change. Pay attention to those suggestions, and you will save a lot of debugging time. Here, I have listed a few common actions you could take:

  • Check for typos or other silly mistakes. It is super common even for experts to make ”silly” mistakes. If you misspell the name of a variable, it is highly likely that the model will not even run. But sometimes the mistake is more subtle, and you still get a syntactically valid model that runs, but with the wrong semantics.

  • Increase the number of samples. This might help for very mild problems, like when you’re close to the target ESS (or MCSE), or when ^R is slightly higher than 1.01 but not too much.

  • Remove some samples from the beginning of the trace. When checking a trace plot, you may observe that a few samples from the first few steps have overall higher or...

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