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Learning Bayesian Models with R

You're reading from   Learning Bayesian Models with R Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems

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Product type Paperback
Published in Oct 2015
Publisher Packt
ISBN-13 9781783987603
Length 168 pages
Edition 1st Edition
Languages
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Author (1):
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Hari Manassery Koduvely Hari Manassery Koduvely
Author Profile Icon Hari Manassery Koduvely
Hari Manassery Koduvely
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Table of Contents (11) Chapters Close

Preface 1. Introducing the Probability Theory FREE CHAPTER 2. The R Environment 3. Introducing Bayesian Inference 4. Machine Learning Using Bayesian Inference 5. Bayesian Regression Models 6. Bayesian Classification Models 7. Bayesian Models for Unsupervised Learning 8. Bayesian Neural Networks 9. Bayesian Modeling at Big Data Scale Index

The arm package


In this chapter, for the purpose of illustrating Bayesian regression models, we will use the arm package of R. This package was developed by Andrew Gelman and co-workers, and it can be downloaded from the website at http://CRAN.R-project.org/package=arm.

The arm package has the bayesglm function that implements the Bayesian generalized linear model with an independent normal, t, or Cauchy prior distributions, for the model coefficients. We will use this function to build Bayesian regression models.

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