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Learning Bayesian Models with R

You're reading from Learning Bayesian Models with R

Product type Book

Published in Oct 2015

Publisher Packt

ISBN-13 9781783987603

Pages 168 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Hari Manassery Koduvely

Table of Contents (16) Chapters

Learning Bayesian Models with R

Credits

About the Author

About the Reviewers

www.PacktPub.com

Preface

1. Introducing the Probability Theory

2. The R Environment

3. Introducing Bayesian Inference

4. Machine Learning Using Bayesian Inference

5. Bayesian Regression Models

6. Bayesian Classification Models

7. Bayesian Models for Unsupervised Learning

8. Bayesian Neural Networks

9. Bayesian Modeling at Big Data Scale

Index

Setting up the R environment and packages

R is a free software under GNU open source license. R comes with a basic package and also has a large number of user-contributed packages for advanced analysis and modeling. It also has a nice graphics user interface-based editor called RStudio. In this section, we will learn how to download R, set up the R environment in your computer, and write a simple R program.

Installing R and RStudio

The Comprehensive R Archive Network (CRAN) hosts all releases of R and the contributed packages. R for Windows can be installed by downloading the binary of the base package from http://cran.r-project.org; a standard installation should be sufficient. For Linux and Mac OS X, the webpage gives instructions on how to download and install the software. At the time of writing this book, the latest release was version 3.1.2. Various packages need to be installed separately from the package page. One can install any package from the R command prompt using the following...

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Authors (1)

Hari Manassery Koduvely

Dr. Hari M. Koduvely is an experienced data scientist working at the Samsung R&D Institute in Bangalore, India. He has a PhD in statistical physics from the Tata Institute of Fundamental Research, Mumbai, India, and post-doctoral experience from the Weizmann Institute, Israel, and Georgia Tech, USA. Prior to joining Samsung, the author has worked for Amazon and Infosys Technologies, developing machine learning-based applications for their products and platforms. He also has several publications on Bayesian inference and its applications in areas such as recommendation systems and predictive health monitoring. His current interest is in developing large-scale machine learning methods, particularly for natural language understanding.

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