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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

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789341652
Length 356 pages
Edition 2nd Edition
Languages
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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 (11) Chapters Close

Preface 1. Thinking Probabilistically 2. Programming Probabilistically FREE CHAPTER 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?
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The GLM module

As we discussed at the beginning of this chapter, linear models are very useful statistical tools. Extensions such as the ones we saw in this chapter make them even more general tools. For that reason, PyMC3 includes a module to simplify the creation of linear models: the Generalized Liner Model (GLM) module. For example, a simple linear regression will be as follows:

with pm.Model() as model: 
    glm.glm('y ~ x', data) 
    trace = sample(2000) 

The second line of the preceding code takes care of adding priors for the intercept and for the slope. By default, the intercept is assigned a flat prior, and the slopes an prior. Note that the maximum a posteriori (MAP) of the default model will be essentially equivalent to the one obtained using the ordinary least squared method. These is totally fine as a default linear regression; you can change it using...

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