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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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Toc

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

Index

A

  • Akaike information criterion (AIC) / Laplace approximation
  • allele frequencies
    • about / Beta distribution
  • arm package
    • about / The arm package
  • association rule mining
    • about / An overview of common machine learning tasks

B

  • Bayesian averaging
    • about / Bayesian averaging
  • Bayesian classification models
    • exercises / Exercises
  • Bayesian inference
    • Bayesian view of uncertainty / Bayesian view of uncertainty
    • exercises / Exercises
    • for machine learning / Why Bayesian inference for machine learning?
  • Bayesian information criterion (BIC) / Laplace approximation
  • Bayesian logistic regression model
    • about / The Bayesian logistic regression model
    • BayesLogit R package / The BayesLogit R package
    • dataset / The dataset
    • training, preparing for / Preparation of the training and testing datasets
    • datasets testing, preparing for / Preparation of the training and testing datasets
    • using / Using the Bayesian logistic...
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