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

Product typeBook
Published inJan 2024
Reading LevelExpert
PublisherPackt
ISBN-139781805127161
Edition3rd Edition
Languages
Python
Concepts
Machine Learning
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Author (1)
Osvaldo Martin
Osvaldo Martin
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Osvaldo Martin

Osvaldo Martin is a researcher at CONICET, in Argentina. He has experience using Markov Chain Monte Carlo methods to simulate molecules and perform Bayesian inference. He loves to use Python to solve data analysis problems. He is especially motivated by the development and implementation of software tools for Bayesian statistics and probabilistic modeling. He is an open-source developer, and he contributes to Python libraries like PyMC, ArviZ and Bambi among others. He is interested in all aspects of the Bayesian workflow, including numerical methods for inference, diagnosis of sampling, evaluation and criticism of models, comparison of models and presentation of results.
Read more about Osvaldo Martin

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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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Bayesian Analysis with Python - Third Edition
Published in: Jan 2024Publisher: PacktISBN-13: 9781805127161
© 2024 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Osvaldo Martin

Osvaldo Martin is a researcher at CONICET, in Argentina. He has experience using Markov Chain Monte Carlo methods to simulate molecules and perform Bayesian inference. He loves to use Python to solve data analysis problems. He is especially motivated by the development and implementation of software tools for Bayesian statistics and probabilistic modeling. He is an open-source developer, and he contributes to Python libraries like PyMC, ArviZ and Bambi among others. He is interested in all aspects of the Bayesian workflow, including numerical methods for inference, diagnosis of sampling, evaluation and criticism of models, comparison of models and presentation of results.
Read more about Osvaldo Martin

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