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Bayesian Analysis with Python

You're reading from Bayesian Analysis with Python Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ

Product type Paperback

Published in Dec 2018

Publisher Packt

ISBN-13 9781789341652

Length 356 pages

Edition 2nd Edition

Languages

Python

Concepts

Data Science

Author (1):

Osvaldo Martin

View More author details

Table of Contents (11) Chapters

Preface

1. Thinking Probabilistically FREE CHAPTER

2. Programming Probabilistically

3. Modeling with Linear Regression

4. Generalizing Linear Models

5. Model Comparison

6. Mixture Models

7. Gaussian Processes

8. Inference Engines

9. Where To Go Next?

10. Other Books You May Enjoy

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Diagnosing the samples

This section is focused on diagnostic samples for Metropolis and NUTS. Since we are approximating the posterior with a finite number of samples, is important to check whether we have a valid sample—otherwise any analysis from it will be totally flawed. There are several tests we can perform, some are visual and some are quantitative. These tests are designed to spot problems with our samples, but they are unable to prove we have the correct distribution; they can only provide evidence that the sample seems reasonable. If we find problems with the sample, the are many solutions to try:

Increase the number of samples.
Remove a number of samples from the beginning of the trace. This is known as burn-in. The PyMC3 tuning phase helps reduce the need for burn-in.
Modify sampler parameters, such as increasing the length of the tuning phase, or increase...

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Authors (1)

Osvaldo Martin

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.

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