Graphical Models
Representations for Learning, Reasoning and Data Mining
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Beschreibung
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.
Autor*in
Christian Borgelt
Themen in »Graphical Models«
Angewandte Wahrscheinlichkeitsrechnung u. Statistik Applied Probability & Statistics Bayesian Analysis Bayes-Verfahren Computational & Graphical Statistics Computertechnik Data Mining Data Mining Statistics Datenanalyse Rechnergestützte u. graphische Statistik Statistics Statistik
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"The text provides graduate students, and researchers withall the necessary background material, including modelling underuncertainty, decomposition of distributions, graphicalrepresentation of distributions, and applications relating tographical models and problems for further research." (Zentralblatt Math, 1 August 2013)"All of the necessary background is provided, with material onmodeling under uncertainty and imprecision modeling, decompositionof distributions, graphical representation of distributions,applications relating to graphical models, and problems for furtherresearch." (Book News, December 2009)
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