Graphical Models for Categorical Data

This book arises out of a short course given in a Séminaires Européens de Statistiques (SemStat) meeting at the European Institute for Statistics, Probability, Stochastic Operations Research and their Applications (EURANDOM) in Eindhoven, The Netherlands, over March 7–10, 2017. This SemStat meeting was organized as a part of the COST Action “European Cooperation for Statistics of Network Data Science” (COSTNET, CA15109) with the aim of introducing early career researchers to the field of statistical network science. In this perspective, the material presented here concerns the theory of graphical models and includes well-established methodology from the early developments in this field, but also the theory of models introduced more recently in the graphical model literature. The focus is on the discrete case where all the variables involved in the analysis are categorical and, in this context, classical and more recent results are presented in a unified way.

For advanced students of network data science, this compact account covers both well-established methodology and the theory of models recently introduced in the graphical model literature. It focuses on the discrete case where all variables involved are categorical and, in this context, it achieves a unified presentation of classical and recent results.