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Learning Bayesian Models with R

By : Hari Manassery Koduvely
Book Image

Learning Bayesian Models with R

By: Hari Manassery Koduvely

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Learning Bayesian Models with R
Hari Manassery Koduvely
Oct, 2015 | 168 pages
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Table of Contents (16 chapters)
Learning Bayesian Models with R
Learning Bayesian Models with R
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Introducing the Probability Theory
Introducing the Probability Theory
Probability distributions
Conditional probability
Bayesian theorem
Marginal distribution
Expectations and covariance
Exercises
References
Summary
2
The R Environment
The R Environment
Setting up the R environment and packages
Managing data in R
Writing R programs
Data visualization
Sampling
Exercises
References
Summary
3
Introducing Bayesian Inference
Introducing Bayesian Inference
Bayesian view of uncertainty
Exercises
References
Summary
4
Machine Learning Using Bayesian Inference
Machine Learning Using Bayesian Inference
Why Bayesian inference for machine learning?
Model overfitting and bias-variance tradeoff
Selecting models of optimum complexity
Bayesian averaging
An overview of common machine learning tasks
References
Summary
5
Bayesian Regression Models
Bayesian Regression Models
Generalized linear regression
The arm package
The Energy efficiency dataset
Regression of energy efficiency with building parameters
Simulation of the posterior distribution
Exercises
References
Summary
6
Bayesian Classification Models
Bayesian Classification Models
Performance metrics for classification
The Naïve Bayes classifier
The Bayesian logistic regression model
Exercises
References
Summary
7
Bayesian Models for Unsupervised Learning
Bayesian Models for Unsupervised Learning
Bayesian mixture models
Topic modeling using Bayesian inference
R packages for LDA
Exercises
References
Summary
8
Bayesian Neural Networks
Bayesian Neural Networks
Two-layer neural networks
Bayesian treatment of neural networks
The brnn R package
Deep belief networks and deep learning
Exercises
References
Summary
9
Bayesian Modeling at Big Data Scale
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
Other R packages for large scale machine learning
Exercises
References
Summary
Index
Index

Exercises

  1. Derive the equation for the posterior mean by expanding the square in the exponential for each i, collecting all similar power terms, and making a perfect square again. Note that the product of exponentials can be written as the exponential of a sum of terms.

  2. For this exercise, we use the dataset corresponding to Smartphone-Based Recognition of Human Activities and Postural Transitions, from the UCI Machine Learning repository (https://archive.ics.uci.edu/ml/datasets/Smartphone-Based+Recognition+of+Human+Activities+and+Postural+Transitions). It contains values of acceleration taken from an accelerometer on a smartphone. The original dataset contains x, y, and z components of the acceleration and the corresponding timestamp values. For this exercise, we have used only the two horizontal components of the acceleration x and y. In this exercise, let's assume that the acceleration follows a normal distribution. Let's also assume a normal prior distribution for the mean values of...

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