MARC record from Internet Archive

LEADER: 05767cam 2201105Ii 4500
001 ocn867318552
003 OCoLC
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008 140104s2013 caua ob 000 0 eng d
006 m o d
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020 $z9781627051972$q(pbk.)
024 7 $a10.2200/S00529ED1V01Y201308AIM023$2doi
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050 4 $aQA279.5$b.D43 2013
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082 04 $a006.3/1$a006.31
100 1 $aDechter, Rina,$d1950-$eauthor.
245 10 $aReasoning with probabilistic and deterministic graphical models :$bexact algorithms /$cRina Dechter.
264 1 $a[San Rafael, California] :$bMorgan & Claypool Publishers,$c[2013]
264 4 $c©2013
300 $a1 online resource (xiv, 177 pages) :$billustrations
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
490 1 $aSynthesis lectures on artificial intelligence and machine learning,$x1939-4616 ;$v#23
504 $aIncludes bibliographical references (pages 167-176).
505 0 $aWhat 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.
520 $aGraphical 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.
588 0 $aOnline resource; title from PDF title page (Morgan & Claypool, viewed Jan. 8, 2014).
650 0 $aGraphical modeling (Statistics)
650 0 $aBayesian statistical decision theory.
650 0 $aReasoning.
650 0 $aAlgorithms.
650 0 $aMachine learning.
650 7 $aCOMPUTERS$xGeneral.$2bisacsh
650 7 $aAlgorithms.$2fast$0(OCoLC)fst00805020
650 7 $aBayesian statistical decision theory.$2fast$0(OCoLC)fst00829019
650 7 $aGraphical modeling (Statistics)$2fast$0(OCoLC)fst00946659
650 7 $aMachine learning.$2fast$0(OCoLC)fst01004795
650 7 $aReasoning.$2fast$0(OCoLC)fst01091282
653 $agraphical models
653 $aBayesian networks
653 $aconstraint networks
653 $aMarkov networks
653 $ainduced-width
653 $atreewidth
653 $acycle-cutset
653 $aloop-cutset
653 $apseudo-tree
653 $abucket-elimination
653 $avariable-elimination
653 $aAND/OR search
653 $aconditioning
653 $areasoning
653 $ainference
653 $aknowledge representation
655 0 $aElectronic books.
655 4 $aElectronic books.
776 08 $iPrint version:$aDechter, Rina, 1950-$tReasoning with probabilistic and deterministic graphical models.$d[San Rafael, Calif.] : Morgan & Claypool Publishers, [2013]$z9781627051972$w(OCoLC)867613530
830 0 $aSynthesis lectures on artificial intelligence and machine learning ;$v#23.
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856 40 $3IEEE Xplore$uhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6812756
856 40 $3Morgan & Claypool$uhttp://www.morganclaypool.com/doi/abs/10.2200/S00529ED1V01Y201308AIM023
856 40 $3Safari Books Online$uhttp://proquest.safaribooksonline.com/9781627051972
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856 42 $3IEEE Xplore$uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=6812756
856 48 $3Abstract with links to full text$uhttps://doi.org/10.2200/S00529ED1V01Y201308AIM023
856 48 $uhttps://doi.org/10.2200/S00529ED1V01Y201308AIM023
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