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. The use of graphical models in applied statistics has increased considerably in recent years. At the same time the field of data mining has developed as a response to the large amounts of available data. This book addresses the overlap between these two important areas, highlighting the advantages of using graphical models for data analysis and mining. The Authors focus not only on probabilistic models such as Bayesian and Markov networks but also explore relational and possibilistic graphical models in order to analyse data sets. * Presents all necessary background material including uncertainty and imprecision modeling, distribution decomposition and graphical representation. * Covers Markov, Bayesian, relational and possibilistic networks. * Includes a new chapter on visualization and coverage of clique tree propagation, visualization techniques. * Demonstrates learning algorithms based on a large number of different search methods and evaluation measures. * Includes a comprehensive bibliography and a detailed index. * Features an accompanying website hosting exercises, teaching material and open source software. Researchers and practitioners who use graphical models in their work, graduate students of applied statistics, computer science and engineering will find much of interest in this new edition.