Book Image

Learning Bayesian Models with R

By : Hari Manassery Koduvely
Book Image

Learning Bayesian Models with R

By: Hari Manassery Koduvely

Overview of this book

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 (11 chapters)
10
Index

Summary

In this last chapter of the book, we covered various frameworks to implement large-scale machine learning. These are very useful for Bayesian learning too. For example, to simulate from a posterior distribution, one could run a Gibbs sampling over a cluster of machines. We learned how to connect to Hadoop from R using the RHadoop package and how to use R with Spark using SparkR. We also discussed how to set up clusters in cloud services such as AWS and how to run Spark on them. Some of the native parallelization frameworks such as parallel and foreach functions were also covered.

The overall aim of this book was to introduce readers to the area of Bayesian modeling using R. Readers should have gained a good grasp of theory and concepts behind Bayesian machine learning models. Since the examples were mainly given for the purposes of illustration, I urge readers to apply these techniques to real-world problems to appreciate the subject of Bayesian inference more deeply.