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

The brnn R package


The brnn package was developed by Paulino Perez Rodriguez and Daniel Gianola, and it implements the two-layer Bayesian regularized neural network described in the previous section. The main function in the package is brnn( ) that can be called using the following command:

>brnn(x,y,neurons,normalize,epochs,…,Monte_Carlo,…)

Here, x is an n x p matrix where n is the number of data points and p is the number of variables; y is an n dimensional vector containing target values. The number of neurons in the hidden layer of the network can be specified by the variable neurons. If the indicator function normalize is TRUE, it will normalize the input and output, which is the default option. The maximum number of iterations during model training is specified using epochs. If the indicator binary variable Monte_Carlo is true, then an MCMC method is used to estimate the trace of the inverse of the Hessian matrix A.

Let us try an example with the Auto MPG dataset that we used in Chapter...