Graphical Models
Representations for Learning, Reasoning and Data Mining
Samenvatting
Provides a self-contained introduction to learning relational, probabilistic and possibilistic networks from data All basic concepts carefully explained and illustrated by examples throughout Contains background material including graphical representation, including Markov and Bayesian Networks. Includes a comprehensive bibliography.

