What this book covers
This book gives 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 most important machine learning methods, both supervised
learning 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 datasets
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 state-of-the-art topics covered in the chapters are
Bayesian regression using linear and generalized linear models, Bayesian classification
using logistic regression, classification of text data using Naïve Bayes models, and
Bayesian mixture models and topic modeling using Latent Dirichlet allocation.
The last two chapters are devoted to the latest developments in the field. One chapter
discusses 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 frameworks such as Hadoop and Spark.
Chapter 1, Introducing the Probability Theory, covers the foundational concepts of
probability theory, particularly those aspects required for learning Bayesian inference,
which are presented to you in a simple and coherent manner.
Chapter 2, The R Environment, introduces you to the R environment. After reading
through this chapter, you will learn how to import data into R, make a selection of
subsets of data for its analysis, and write simple R programs using functions and control
structures. Also, you will get familiar with the graphical capabilities of R and some
advanced capabilities such as loop functions.
Chapter 3, Introducing Bayesian Inference, introduces you to the Bayesian statistic
framework. This chapter includes a description of the Bayesian theorem, concepts such
as prior and posterior probabilities, and different methods to estimate posterior
distribution such as MAP estimates, Monte Carlo simulations, and variational estimates.
Chapter 4, Machine Learning Using Bayesian Inference, gives an overview of what
machine learning is and what some of its high-level tasks are. This chapter also
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