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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Learning Bayesian Models with R

Dr. Hari M. Koduvely

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Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems
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Book Details

ISBN 139781783987603
Paperback168 pages

Book Description

Bayesian Inference provides a unified framework to deal with all sorts of uncertainties when learning patterns form data using machine learning models and use it for predicting future observations. However, learning and implementing Bayesian models is not easy for data science practitioners due to the level of mathematical treatment involved. Also, applying Bayesian methods to real-world problems requires high computational resources. With the recent advances in computation and several open sources packages available in R, Bayesian modeling has become more feasible to use for practical applications today. Therefore, it would be advantageous for all data scientists and engineers to understand Bayesian methods and apply them in their projects to achieve better results.

Learning Bayesian Models with R starts by giving you a comprehensive coverage of the Bayesian Machine Learning models and the R packages that implement them. It begins with an introduction to the fundamentals of probability theory and R programming for those who are new to the subject. Then the book covers some of the important machine learning methods, both supervised and unsupervised learning, implemented using Bayesian Inference and R.

Every chapter begins with a theoretical description of the method explained in a very simple manner. Then, relevant R packages are discussed and some illustrations using data sets from the UCI Machine Learning repository are given. Each chapter ends with some simple exercises for you to get hands-on experience of the concepts and R packages discussed in the chapter.

The last chapters are devoted to the latest development in the field, specifically Deep Learning, which uses a class of Neural Network models that are currently at the frontier of Artificial Intelligence. The book concludes with the application of Bayesian methods on Big Data using the Hadoop and Spark frameworks.

Table of Contents

Chapter 1: Introducing the Probability Theory
Probability distributions
Conditional probability
Bayesian theorem
Marginal distribution
Expectations and covariance
Exercises
References
Summary
Chapter 2: The R Environment
Setting up the R environment and packages
Managing data in R
Writing R programs
Data visualization
Sampling
Exercises
References
Summary
Chapter 3: Introducing Bayesian Inference
Bayesian view of uncertainty
Exercises
References
Summary
Chapter 4: 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
Chapter 5: 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
Chapter 6: Bayesian Classification Models
Performance metrics for classification
The Naïve Bayes classifier
The Bayesian logistic regression model
Exercises
References
Summary
Chapter 7: Bayesian Models for Unsupervised Learning
Bayesian mixture models
Topic modeling using Bayesian inference
R packages for LDA
Exercises
References
Summary
Chapter 8: 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
Chapter 9: 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

What You Will Learn

  • Set up the R environment
  • Create a classification model to predict and explore discrete variables
  • Get acquainted with Probability Theory to analyze random events
  • Build Linear Regression models
  • Use Bayesian networks to infer the probability distribution of decision variables in a problem
  • Model a problem using Bayesian Linear Regression approach with the R package BLR
  • Use Bayesian Logistic Regression model to classify numerical data
  • Perform Bayesian Inference on massively large data sets using the MapReduce programs in R and Cloud computing

Authors

Table of Contents

Chapter 1: Introducing the Probability Theory
Probability distributions
Conditional probability
Bayesian theorem
Marginal distribution
Expectations and covariance
Exercises
References
Summary
Chapter 2: The R Environment
Setting up the R environment and packages
Managing data in R
Writing R programs
Data visualization
Sampling
Exercises
References
Summary
Chapter 3: Introducing Bayesian Inference
Bayesian view of uncertainty
Exercises
References
Summary
Chapter 4: 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
Chapter 5: 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
Chapter 6: Bayesian Classification Models
Performance metrics for classification
The Naïve Bayes classifier
The Bayesian logistic regression model
Exercises
References
Summary
Chapter 7: Bayesian Models for Unsupervised Learning
Bayesian mixture models
Topic modeling using Bayesian inference
R packages for LDA
Exercises
References
Summary
Chapter 8: 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
Chapter 9: 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

Book Details

ISBN 139781783987603
Paperback168 pages
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