book

Learning Probabilistic Graphical Models in R

by David Bellot

April 2016

Beginner to intermediate

250 pages

5h 38m

English

Packt Publishing

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What this book covers

Downloading the example codeErrataPiracyQuestions
Machine learning
Beliefs and uncertainty as probabilitiesConditional probabilityProbability calculus and random variablesSample space, events, and probabilityRandom variables and probability calculusJoint probability distributionsBayes' ruleInterpreting the Bayes' formulaA first example of Bayes' ruleA first example of Bayes' rule in R
Probabilistic modelsGraphs and conditional independenceFactorizing a distributionDirected modelsUndirected modelsExamples and applications
Building graphical modelsTypes of random variableBuilding graphsProbabilistic expert systemBasic structures in probabilistic graphical models
The sprinkler exampleThe medical expert systemModels with more than two layersTree structure
Introduction
How are empirical and model distribution related?The ML algorithm and its implementation in RApplication
Latent variables
Derivation of the EM algorithmApplying EM to graphical models
The Naive Bayes modelRepresentationLearning the Naive Bayes modelBayesian Naive Bayes
The prior distributionThe posterior distribution with the conjugacy propertyWhich values should we choose for the Beta parameters?
Definition
Sampling from a distribution
Standard distributions
An implementation in R
An implementation in R
General idea of the methodThe Metropolis-Hastings algorithm
Installing Stan and RStanA simple example in RStan
Linear regressionEstimating the parameters
Over-fitting a modelGraphical model of a linear modelPosterior distributionImplementation in RA stable implementationMore packages in R
Mixture models
The LDA modelVariational inferenceExamples
ReferencesBooks on the Bayesian theoryBooks on machine learningPapers

Content preview from Learning Probabilistic Graphical Models in R

Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "We can also mention the arm package, which provides Bayesian versions of glm() and polr() and implements hierarchical models."

Any command-line input or output is written as follows:

pred_sigma <- sqrt(sigma^2 + apply((T%*%posterior_sigma)*T, MARGIN=1, FUN=sum))
upper_bound <- T%*%posterior_beta + qnorm(0.95)*pred_sigma
lower_bound <- T%*%posterior_beta - qnorm(0.95)*pred_sigma