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Bayesian Analysis with Python

Bayesian Analysis with Python

By : Osvaldo Martin
3.2 (17)
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Bayesian Analysis with Python

Bayesian Analysis with Python

3.2 (17)
By: Osvaldo Martin

Overview of this book

The second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models. The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others. By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to.
Table of Contents (11 chapters)
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Preface
Preface
Who this book is for
What this book covers
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1
Thinking Probabilistically
Thinking Probabilistically
Statistics, models, and this book's approach
Probability theory
Single-parameter inference
Communicating a Bayesian analysis
Posterior predictive checks
Summary
Exercises
2
Programming Probabilistically
Programming Probabilistically
Probabilistic programming
PyMC3 primer
Summarizing the posterior
Gaussians all the way down
Groups comparison
Hierarchical models
Summary
Exercises
3
Modeling with Linear Regression
Modeling with Linear Regression
Simple linear regression
Robust linear regression
Hierarchical linear regression
Polynomial regression
Multiple linear regression
Variable variance
Summary
Exercises
4
Generalizing Linear Models
Generalizing Linear Models
Generalized linear models
Logistic regression
Multiple logistic regression
Poisson regression
Robust logistic regression
The GLM module
Summary
Exercises
5
Model Comparison
Model Comparison
Posterior predictive checks
Occam's razor – simplicity and accuracy
Information criteria
Bayes factors
Regularizing priors
WAIC in depth
Summary
Exercises
6
Mixture Models
Mixture Models
Mixture models
Finite mixture models
Non-finite mixture model
Continuous mixtures
Summary
Exercises
7
Gaussian Processes
Gaussian Processes
Linear models and non-linear data
Modeling functions
Gaussian process regression
Regression with spatial autocorrelation
Gaussian process classification
Cox processes
Summary
Exercises
8
Inference Engines
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Inference Engines
Inference engines
Non-Markovian methods
Markovian methods
Diagnosing the samples
Summary
Exercises
9
Where To Go Next?
Where To Go Next?
10
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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...
End of Section 5
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