Details

Title PROBABILISTIC CONDITIONAL INDEPENDENCE STRUCTURES (HB)
Author Milan Studeny
Binding Hardback
Edition U. S. EDITION
Condition New
Pages 285
Volumes 1
Language ENG
Publisher Springer
Publication date 2005-01-07
Illustrated Yes
Features Bibliography, Illustrated, Index, Table of Contents
Bookseller's Inventory # BWP-9781852338916
ISBN 9781852338916 / 1852338911
Weight 1.21 lbs (0.55 kg)
Dimensions 9.46 x 6.5 x 0.74 in (24.03 x 16.51 x 1.88 cm)
Category Mathematics
Library of Congress subjects Decision making - Mathematical models, Distribution (Probability theory)
Library of Congress Catalogue Number 2004059834
Dewey Decimal Code 519.5
Quantity available 500

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From the publisher

Conditional independence is a topic that lies between statistics and artificial intelligence. Probabilistic Conditional Independence Structures provides the mathematical description of probabilistic conditional independence structures; the author uses non-graphical methods of their description, and takes an algebraic approach. The monograph presents the methods of structural imsets and supermodular functions, and deals with independence implication and equivalence of structural imsets. Motivation, mathematical foundations and areas of application are included, and a rough overview of graphical methods is also given. In particular, the author has been careful to use suitable terminology, and presents the work so that it will be understood by both statisticians, and by researchers in artificial intelligence. The necessary elementary mathematical notions are recalled in an appendix.

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From the rear cover

Conditional independence is a topic that lies between statistics and artificial intelligence. Probabilistic Conditional Independence Structures provides the mathematical description of probabilistic conditional independence structures; the author uses non-graphical methods of their description, and takes an algebraic approach.

The monograph presents the methods of structural imsets and supermodular functions, and deals with independence implication and equivalence of structural imsets. Motivation, mathematical foundations and areas of application are included, and a rough overview of graphical methods is also given. In particular, the author has been careful to use suitable terminology, and presents the work so that it will be understood by both statisticians, and by researchers in artificial intelligence. The necessary elementary mathematical notions are recalled in an appendix.

Probabilistic Conditional Independence Structures will be a valuable new addition to the literature, and will interest applied mathematicians, statisticians, informaticians, computer scientists and probabilists with an interest in artificial intelligence. The book may also interest pure mathematicians as open problems are included.

Milan Studen is a senior research worker at the Academy of Sciences of the Czech Republic.

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