Graphical models are of increasing importance in applied statistics, and
in particular in data mining. Providing a self-contained introduction
and overview to learning relational, probabilistic, and possibilistic
networks from data, this second edition of Graphical Models is
thoroughly updated to include the latest research in this burgeoning
field, including a new chapter on visualization. The text provides
graduate students, and researchers with all the necessary background
material, including modelling under uncertainty, decomposition of
distributions, graphical representation of distributions, and
applications relating to graphical models and problems for further
research.