Ebook: Graphical Models with R

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.

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.

Soren Hojsgaard is Associate Professor in Statistics and Head of the Department of Mathematical Sciences at Aalborg University.

David Edwards is Associate Professor at the Department of Molecular Biology and Genetics, Aarhus University.

Steffen Lauritzen is Professor of Statistics and Head of the Department of Statistics at the University of Oxford.

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.

Soren Hojsgaard is Associate Professor in Statistics and Head of the Department of Mathematical Sciences at Aalborg University.

David Edwards is Associate Professor at the Department of Molecular Biology and Genetics, Aarhus University.

Steffen Lauritzen is Professor of Statistics and Head of the Department of Statistics at the University of Oxford.

Content:
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

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.

Soren Hojsgaard is Associate Professor in Statistics and Head of the Department of Mathematical Sciences at Aalborg University.

David Edwards is Associate Professor at the Department of Molecular Biology and Genetics, Aarhus University.

Steffen Lauritzen is Professor of Statistics and Head of the Department of Statistics at the University of Oxford.

Content:
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
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