Learning Bayesian Models with R电子书

Learning Bayesian Models with R

Table of Contents

Learning Bayesian Models with R

Credits

About the Author

About the Reviewers

www.PacktPub.com

Support files, eBooks, discount offers, and more

Why subscribe?

Free access for Packt account holders

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Errata

Piracy

Questions

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

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

Exercises

References

Summary

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

The Metropolis-Hasting algorithm

R packages for the Metropolis-Hasting algorithm

Gibbs sampling

R packages for Gibbs sampling

Variational approximation

Prediction of future observations

Exercises

References

Summary

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

Summary

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

Exercises

References

Summary

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

Exercises

References

Summary

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

Exercises

References

Summary

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

Exercises

References

Summary

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

Installing R and RStudio

Running Spark on EC2

Microsoft Azure

IBM Bluemix

Other R packages for large scale machine learning

The parallel R package

The foreach R package

Exercises

References

Summary

Index