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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 2. Chapter 2 Programming Probabilistically FREE CHAPTER 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

5.4 Calculating predictive accuracy with ArviZ

Fortunately, calculating WAIC and LOO with ArviZ is very simple. We just need to be sure that the Inference Data has the log-likelihood group. When computing a posterior with PyMC, this can be achieved by doing pm.sample(idata_kwargs="log_likelihood": True). Now, let’s see how to compute LOO:

Code 5.3

az.loo(idata_l)

Computed from 8000 posterior samples and 33 observations log-likelihood matrix.

         Estimate       SE elpd_loo   -14.31     2.67
p_loo        2.40        -
------

Pareto k diagnostic values:
                         Count...
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