Graphical Models with R

✍ Scribed by Søren Højsgaard, David Edwards, Steffen Lauritzen (auth.)

Publisher: Springer-Verlag New York
Year: 2012
Tongue: English
Leaves: 186
Series: Use R!
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

Graphical models in their modern form have been around since the late 1970s and appear today in many areas of the sciences. Along with the ongoing developments of graphical models, a number of different graphical modeling software programs have been written over the years. In recent years many of these software developments have taken place within the R community, either in the form of new packages or by providing an R interface to existing software. This book attempts to give the reader a gentle introduction to graphical modeling using R and the main features of some of these packages. In addition, the book provides examples of how more advanced aspects of graphical modeling can be represented and handled within R. Topics covered in the seven chapters include graphical models for contingency tables, Gaussian and mixed graphical models, Bayesian networks and modeling high dimensional data.

✦ Table of Contents

Front Matter....Pages I-IX
Graphs and Conditional Independence....Pages 1-25
Log-Linear Models....Pages 27-49
Bayesian Networks....Pages 51-76
Gaussian Graphical Models....Pages 77-116
Mixed Interaction Models....Pages 117-143
Graphical Models for Complex Stochastic Systems....Pages 145-158
High Dimensional Modelling....Pages 159-174
Back Matter....Pages 175-182

✦ Subjects

Statistical Theory and Methods; Statistics, general

📜 SIMILAR VOLUMES

Graphical Models with R

📁 Graphical Models with R

✍ Søren Højsgaard, David Edwards, Steffen Lauritzen (auth.) 📂 Library 📅 2012 🏛 Springer-Verlag New York 🌐 English

<p><p>Graphical models in their modern form have been around since the late 1970s and appear today in many areas of the sciences. Along with the ongoing developments of graphical models, a number of different graphical modeling software programs have been written over the years. In recent years many

Graphical models with R

📁 Graphical models with R

✍ Edwards, David;Højsgaard, Søren;Lauritzen, Steffen L 📂 Library 📅 2012 🏛 Springer 🌐 English

Graphical Models with R

📁 Graphical Models with R

✍ Søren Højsgaard, David Edwards, Steffen Lauritzen (auth.) 📂 Library 📅 2012 🏛 Springer 🌐 English

Graphical Models with R (Use R!)

📁 Graphical Models with R (Use R!)

✍ Søren Højsgaard 📂 Library 📅 2012 🏛 Springer 🌐 English

This book offers an introduction to graphical modeling using R and the main features of some of these packages. It provides examples of how more advanced aspects of graphical modeling can be represented and handled within R.

Graphical Models with R (Use R!)

📁 Graphical Models with R (Use R!)

✍ Søren Højsgaard 📂 Library 📅 2012 🏛 Springer 🌐 English

Learning Probabilistic Graphical Models

📁 Learning Probabilistic Graphical Models in R: Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R

✍ David Bellot 📂 Library 📅 2016 🏛 Packt Publishing 🌐 English

Probabilistic graphical models (PGM, also known as graphical models) are a marriage between probability theory and graph theory. Generally, PGMs use a graph-based representation. Two branches of graphical representations of distributions are commonly used, namely Bayesian networks and Markov network