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Learning Bayesian Models with R

You're reading from   Learning Bayesian Models with R Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems

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Product type Paperback
Published in Oct 2015
Publisher Packt
ISBN-13 9781783987603
Length 168 pages
Edition 1st Edition
Languages
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Author (1):
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Hari Manassery Koduvely Hari Manassery Koduvely
Author Profile Icon Hari Manassery Koduvely
Hari Manassery Koduvely
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Table of Contents (11) Chapters Close

Preface 1. Introducing the Probability Theory FREE CHAPTER 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

Other R packages for large scale machine learning


Apart from RHadoop and SparkR, there are several other native R packages specifically built for large-scale machine learning. Here, we give a brief overview of them. Interested readers should refer to CRAN Task View: High-Performance and Parallel Computing with R (reference 10 in the References section of the chapter).

Though R is single-threaded, there exists several packages for parallel computation in R. Some of the well-known packages are Rmpi (R version of the popular message passing interface), multicore, snow (for building R clusters), and foreach. From R 2.14.0, a new package called parallel started shipping with the base R. We will discuss some of its features here.

The parallel R package

The parallel package is built on top of the multicore and snow packages. It is useful for running a single program on multiple datasets such as K-fold cross validation. It can be used for parallelizing in a single machine over multiple CPUs/cores...

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