عنوان

Reasoning with probabilistic and deterministic graphical models :

شابک

1681734907

شابک

1681734915

شابک

9781681734903

شابک

9781681734910

شابک اشتباه

1681734907

شابک اشتباه

1681734923

شابک اشتباه

9781681734903

شابک اشتباه

9781681734927

عنوان اصلي

Reasoning with probabilistic and deterministic graphical models :

نام عام مواد

[Book]

ساير اطلاعات عنواني

exact algorithms /

نام نخستين پديدآور

Rina Dechter.

وضعيت ويراست

Second edition.

محل نشرو پخش و غیره

[San Rafael, California] :

نام ناشر، پخش کننده و غيره

Morgan & Claypool Publishers,

تاریخ نشرو بخش و غیره

[2019]

تاریخ نشرو بخش و غیره

©2019

نام خاص و کميت اثر

1 online resource (xiv, 185 pages) :

ساير جزييات

illustrations

عنوان فروست

Synthesis lectures on artificial intelligence and machine learning,

مشخصه جلد

#41

شاپا ي ISSN فروست

1939-4616 ;

متن يادداشت

Includes bibliographical references (pages 175-184).

متن يادداشت

1. Introduction -- 1.1 Probabilistic vs. deterministic models -- 1.2 Directed vs. undirected models -- 1.3 General graphical models -- 1.4 Inference and search-based schemes -- 1.5 Overview of the book

متن يادداشت

2. Defining graphical models -- 2.1 General graphical models -- 2.2 The graphs of graphical models -- 2.2.1 Basic definitions -- 2.2.2 Types of graphs -- 2.3 Constraint networks -- 2.4 Cost networks -- 2.5 Probability networks -- 2.5.1 Bayesian networks -- 2.5.2 Markov networks -- 2.6 Influence diagrams -- 2.7 Mixed networks -- 2.8 Summary and bibliographical notes

متن يادداشت

3. Inference: bucket elimination for deterministic networks -- 3.1 Bucket elimination for constraint networks -- 3.2 Bucket elimination for propositional CNFs -- 3.3 Bucket elimination for linear inequalities -- 3.4 The induced-graph and induced-width -- 3.4.1 Trees -- 3.4.2 Finding good orderings -- 3.5 Chordal graphs -- 3.6 Summary and bibliography notes

متن يادداشت

4. Inference: bucket elimination for probabilistic networks -- 4.1 Belief updating and probability of evidence -- 4.1.1 Deriving BE-bel -- 4.1.2 Complexity of BE-bel -- 4.1.3 The impact of observations -- 4.2 Bucket elimination for optimization tasks -- 4.2.1 A bucket elimination algorithm for mpe -- 4.2.2 A bucket elimination algorithm for map -- 4.3 Bucket elimination for Markov networks -- 4.4 Bucket elimination for influence diagrams -- 4.5 Bucket elimination for cost networks and dynamic programming -- 4.6 Bucket elimination for mixed networks -- 4.7 The general bucket elimination -- 4.8 Summary and bibliographical notes -- 4.9 Appendix: proofs

متن يادداشت

5. Tree-clustering schemes -- 5.1 Bucket-tree elimination -- 5.1.1 Asynchronous bucket-tree propagation -- 5.2 From bucket trees to cluster trees -- 5.2.1 From buckets to clusters -- the short route -- 5.2.2 Acyclic graphical models -- 5.2.3 Tree decomposition and cluster tree elimination -- 5.2.4 Generating tree decompositions -- 5.3 Properties of CTE for general models -- 5.3.1 Correctness of CTE -- 5.3.2 Complexity of CTE -- 5.4 Illustration of CTE for specific models -- 5.4.1 Belief updating and probability of evidence -- 5.4.2 Constraint networks -- 5.4.3 Optimization -- 5.5 Summary and bibliographical notes -- 5.6 Appendix: proofs

متن يادداشت

6. AND/OR search spaces for graphical models -- 6.1 AND/OR search trees -- 6.1.1 Weights of OR-AND arcs -- 6.1.2 Pseudo trees -- 6.1.3 Properties of AND/OR search trees -- 6.2 AND/OR search graphs -- 6.2.1 Generating compact AND/OR search spaces -- 6.2.2 Building context-minimal AND/OR search graphs -- 6.2.3 Size of AND/OR graph -- 6.3 Finding good pseudo-trees -- 6.3.1 Pseudo trees created from induced-graphs -- 6.3.2 Hypergraph decompositions -- 6.4 Value functions of reasoning problems -- 6.4.1 Searching and/or tree (AOT) and and/or graph (AOG) -- 6.5 General AND-OR search -- AO(i) -- 6.5.1 Complexity -- 6.6 AND/OR search algorithms for mixed networks -- 6.6.1 AND-OR-cpe algorithm -- 6.6.2 Constraint propagation in AND-OR-cpe -- 6.6.3 Good and nogood learning -- 6.7 Summary and bibliographical notes -- 6.8 Appendix: proofs

متن يادداشت

7. Combining search and inference: trading space for time -- 7.1 The cutset-conditioning scheme -- 7.1.1 Cutset-conditioning for constraints -- 7.1.2 General cutset-conditioning -- 7.1.3 Alternating conditioning and elimination -- 7.2 The super-cluster schemes -- 7.3 Trading time and space with AND/OR search -- 7.3.1 AND/OR cutset-conditioning -- 7.3.2 Algorithm adaptive caching (AOC.q/) -- 7.3.3 Relations between AOC(q), AO-ALT-VEC(q) and AO-VEC(q) -- 7.3.4 AOC(q) Compared with STCE(q) -- 7.4 Summary and bibliographical notes -- 7.5 Appendix: proofs

متن يادداشت

8. Conclusion -- Bibliography -- Author's biography.

بدون عنوان

0

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8

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8

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8

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8

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8

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8

متن يادداشت

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. This book provides 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. The new edition includes the notion of influence diagrams, which focus on sequential decision making under uncertainty. We believe the principles outlined in the book 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.

منبع سفارش / آدرس اشتراک

01307586

موضوع مستند نشده

Algorithms.

موضوع مستند نشده

Bayesian statistical decision theory.

موضوع مستند نشده

Graphical modeling (Statistics)

موضوع مستند نشده

Machine learning.

موضوع مستند نشده

Reasoning.

موضوع مستند نشده

Algorithms.

موضوع مستند نشده

Bayesian statistical decision theory.

موضوع مستند نشده

COMPUTERS-- General.

موضوع مستند نشده

Graphical modeling (Statistics)

موضوع مستند نشده

Machine learning.

موضوع مستند نشده

Reasoning.

موضوع مستند نشده

COM-- 000000

شماره

ويراست

شماره رده

نشانه اثر

مستند نام اشخاص تاييد نشده

Dechter, Rina,1950-

تاريخ عمليات

20200823052156.0

قواعد فهرست نويسي ( بخش توصيفي )

pn

نام الکترونيکي

نوع ماده

[Book]

تكميل شده

Y