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

Chapter 4. Machine Learning Using Bayesian Inference

Now that we have learned about Bayesian inference and R, it is time to use both for machine learning. In this chapter, we will give an overview of different machine learning techniques and discuss each of them in detail in subsequent chapters. Machine learning is a field at the intersection of computer science and statistics, and a subbranch of artificial intelligence or AI. The name essentially comes from the early works in AI where researchers were trying to develop learning machines that automatically learned the relationship between input and output variables from data alone. Once a machine is trained on a dataset for a given problem, it can be used as a black box to predict values of output variables for new values of input variables.

It is useful to set this learning process of a machine in a mathematical framework. Let X and Y be two random variables such that we seek a learning machine that learns the relationship between...