Graphical Models: Representations for Learning, Reasoning and Data Mining: 704 (Wiley Series in Computational Statistics) - Tapa dura

Acerca del autor

Christian Borgelt, is the Principal researcher at the European Centre for Soft Computing at Otto-von-Guericke University of Magdeburg.

Rudolf Kruse, Professor for Computer Science at Otto-von-Guericke University of Magdeburg.

Matthias Steinbrecher, Department of Knowledge Processing and Language Engineering, School of Computer Science, Universitätsplatz 2,?Magdeburg, Germany.

De la contraportada

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.