Reasoning with Probabilistic and Deterministic Graphical Models (Record no. 84874)

000 -LEADER
fixed length control field 03963nam a22005175i 4500
001 - CONTROL NUMBER
control field 978-3-031-01566-3
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240730163701.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 221111s2013 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783031015663
-- 978-3-031-01566-3
082 04 - CLASSIFICATION NUMBER
Call Number 006.3
100 1# - AUTHOR NAME
Author Kraus, Rina.
245 10 - TITLE STATEMENT
Title Reasoning with Probabilistic and Deterministic Graphical Models
Sub Title Exact Algorithms /
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2013.
300 ## - PHYSICAL DESCRIPTION
Number of Pages IV, 191 p.
490 1# - SERIES STATEMENT
Series statement Synthesis Lectures on Artificial Intelligence and Machine Learning,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Preface -- Introduction -- What are Graphical Models -- Inference: Bucket Elimination for Deterministic Networks -- Inference: Bucket Elimination for Probabilistic Networks -- Tree-Clustering Schemes -- AND/OR Search Spaces and Algorithms for Graphical Models -- Combining Search and Inference: Trading Space for Time -- Conclusion -- Bibliography -- Author's Biography .
520 ## - SUMMARY, ETC.
Summary, etc Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. These problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and probabilistic inference. It is well known that the tasks are computationally hard, but research during the past three decades has yielded a variety of principles and techniques that significantly advanced the state of the art. In this book we provide comprehensive coverage of the primary exact algorithms for reasoning with such models. The main feature exploited by the algorithms is the model's graph. We present inference-based, message-passing schemes (e.g., variable-elimination) and search-based, conditioning schemes (e.g., cycle-cutset conditioning and AND/OR search). Each class possesses distinguished characteristics and in particular has different time vs. space behavior. We emphasize the dependence of both schemes on few graph parameters such as the treewidth, cycle-cutset, and (the pseudo-tree) height. We believe the principles outlined here would serve well in moving forward to approximation and anytime-based schemes. The target audience of this book is researchers and students in the artificial intelligence and machine learning area, and beyond.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-031-01566-3
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2013.
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-- txt
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-- computer
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-- rdamedia
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-- online resource
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-- text file
-- PDF
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial intelligence.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Machine learning.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Neural networks (Computer science) .
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial Intelligence.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Machine Learning.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Mathematical Models of Cognitive Processes and Neural Networks.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 1939-4616
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-- ZDB-2-SXSC

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