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 / Dar. Hari M. Koduvely.

Format:

Book

Author/Creator:

Koduvely, Hari M., author.

Series:

Community experience distilled.

Community experience distilled

Language:

English

Subjects (All):

Machine learning.

Bayesian statistical decision theory.

Neural networks (Computer science).

R (Computer program language).

Physical Description:

1 online resource (168 p.)

Edition:

1st edition

Place of Publication:

Birmingham : Packt Publishing, 2015.

Language Note:

English

System Details:

text file

Summary:

Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems About This Book Understand the principles of Bayesian Inference with less mathematical equations Learn state-of-the art Machine Learning methods Familiarize yourself with the recent advances in Deep Learning and Big Data frameworks with this step-by-step guide Who This Book Is For This book is for statisticians, analysts, and data scientists who want to build a Bayes-based system with R and implement it in their day-to-day models and projects. It is mainly intended for Data Scientists and Software Engineers who are involved in the development of Advanced Analytics applications. To understand this book, it would be useful if you have basic knowledge of probability theory and analytics and some familiarity with the programming language R. What You Will Learn Set up the R environment Create a classification model to predict and explore discrete variables Get acquainted with Probability Theory to analyze random events Build Linear Regression models Use Bayesian networks to infer the probability distribution of decision variables in a problem Model a problem using Bayesian Linear Regression approach with the R package BLR Use Bayesian Logistic Regression model to classify numerical data Perform Bayesian Inference on massively large data sets using the MapReduce programs in R and Cloud computing In Detail 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...

Contents:

Cover

Copyright

Credits

About the Author

About the Reviewers

www.PacktPub.com

Table of Contents

Preface

Chapter 1: Introducing the Probability Theory

Probability distributions

Conditional probability

Bayesian theorem

Marginal distribution

Expectations and covariance

Binomial distribution

Beta distribution

Gamma distribution

Dirichlet distribution

Wishart distribution

Exercises

References

Summary

Chapter 2: The R Environment

Setting up the R environment and packages

Installing R and RStudio

Your first R program

Managing data in R

Data Types in R

Data structures in R

Importing data into R

Slicing and dicing datasets

Vectorized operations

Writing R programs

Control structures

Functions

Scoping rules

Loop functions

lapply

sapply

mapply

apply

tapply

Data visualization

High-level plotting functions

Low-level plotting commands

Interactive graphics functions

Sampling

Random uniform sampling from an interval

Sampling from normal distribution

Chapter 3: Introducing Bayesian Inference

Bayesian view of uncertainty

Choosing the right prior distribution

Non-informative priors

Subjective priors

Conjugate priors

Hierarchical priors

Estimation of posterior distribution

Maximum a posteriori estimation

Laplace approximation

Monte Carlo simulations

Variational approximation

Prediction of future observations

Chapter 4: Machine Learning Using Bayesian Inference

Why Bayesian inference for machine learning?

Model overfitting and bias-variance tradeoff

Selecting models of optimum complexity

Subset selection

Model regularization

Bayesian averaging

An overview of common machine learning tasks.

References

Chapter 5: Bayesian Regression Models

Generalized linear regression

The arm package

The Energy efficiency dataset

Regression of energy efficiency with building parameters

Ordinary regression

Bayesian regression

Simulation of the posterior distribution

Chapter 6: Bayesian Classification Models

Performance metrics for classification

The Naïve Bayes classifier

Text processing using the tm package

Model training and prediction

The Bayesian logistic regression model

The BayesLogit R package

The dataset

Preparation of the training and testing datasets

Using the Bayesian logistic model

Chapter 7: Bayesian Models for Unsupervised Learning

Bayesian mixture models

The bgmm package for Bayesian mixture models

Topic modeling using Bayesian inference

Latent Dirichlet allocation

R packages for LDA

The topicmodels package

The lda package

Chapter 8: Bayesian Neural Networks

Two-layer neural networks

Bayesian treatment of neural networks

The brnn R package

Deep belief networks and deep learning

Restricted Boltzmann machines

Deep belief networks

The darch R package

Other deep learning packages in R

Chapter 9: Bayesian Modeling at Big Data Scale

Distributed computing using Hadoop

RHadoop for using Hadoop from R

Spark - in-memory distributed computing

SparkR

Linear regression using SparkR

Computing clusters on the cloud

Amazon Web Services

Creating and running computing instances on AWS

Running Spark on EC2

Microsoft Azure

IBM Bluemix

Other R packages for large scale machine learning

The parallel R package.

The foreach R package

Index.

Notes:

Includes index.

Includes bibliographical references and index.

Description based on online resource; title from PDF title page (ebrary, viewed January 4, 2016).

ISBN:

9781783987610

1783987618

OCLC:

929988286