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Learning Bayesian Models with R

By : Hari Manassery Koduvely
Book Image

Learning Bayesian Models with R

By: Hari Manassery Koduvely

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Learning Bayesian Models with R
Hari Manassery Koduvely
Oct, 2015 | 168 pages
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Table of Contents (16 chapters)
Learning Bayesian Models with R
Learning Bayesian Models with R
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Introducing the Probability Theory
Introducing the Probability Theory
Probability distributions
Conditional probability
Bayesian theorem
Marginal distribution
Expectations and covariance
Exercises
References
Summary
2
The R Environment
The R Environment
Setting up the R environment and packages
Managing data in R
Writing R programs
Data visualization
Sampling
Exercises
References
Summary
3
Introducing Bayesian Inference
Introducing Bayesian Inference
Bayesian view of uncertainty
Exercises
References
Summary
4
Machine Learning Using Bayesian Inference
Machine Learning Using Bayesian Inference
Why Bayesian inference for machine learning?
Model overfitting and bias-variance tradeoff
Selecting models of optimum complexity
Bayesian averaging
An overview of common machine learning tasks
References
Summary
5
Bayesian Regression Models
Bayesian Regression Models
Generalized linear regression
The arm package
The Energy efficiency dataset
Regression of energy efficiency with building parameters
Simulation of the posterior distribution
Exercises
References
Summary
6
Bayesian Classification Models
Bayesian Classification Models
Performance metrics for classification
The Naïve Bayes classifier
The Bayesian logistic regression model
Exercises
References
Summary
7
Bayesian Models for Unsupervised Learning
Bayesian Models for Unsupervised Learning
Bayesian mixture models
Topic modeling using Bayesian inference
R packages for LDA
Exercises
References
Summary
8
Bayesian Neural Networks
Bayesian Neural Networks
Two-layer neural networks
Bayesian treatment of neural networks
The brnn R package
Deep belief networks and deep learning
Exercises
References
Summary
9
Bayesian Modeling at Big Data Scale
Bayesian Modeling at Big Data Scale
Distributed computing using Hadoop
RHadoop for using Hadoop from R
Spark – in-memory distributed computing
SparkR
Linear regression using SparkR
Computing clusters on the cloud
Other R packages for large scale machine learning
Exercises
References
Summary
Index
Index

Summary

To summarize this chapter, we discussed elements of probability theory; particularly those aspects required for learning Bayesian inference. Due to lack of space, we have not covered many elementary aspects of this subject. There are some excellent books on this subject, for example, books by William Feller (reference 2 in the References section of this chapter), E. T. Jaynes (reference 3 in the References section of this chapter), and M. Radziwill (reference 4 in the References section of this chapter). Readers are encouraged to read these to get a more in-depth understanding of probability theory and how it can be applied in real-life situations.

In the next chapter, we will introduce the R programming language that is the most popular open source framework for data analysis and Bayesian inference in particular.

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