UID:
edocfu_9959739660202883
Umfang:
1 online resource (xxxiii, 387 pages).
ISBN:
1-00-333819-4
,
1-003-33819-4
,
1-000-79255-2
,
87-7022-017-4
Serie:
River Publishers Series in Software Engineering
Anmerkung:
Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Foreword -- Preface -- Acknowledgments -- List of Figures -- List of Tables -- List of Examples -- List of Definitions -- List of Theorems -- List of Abbreviations -- 1: Preliminaries -- 1.1 Orders, Lattices, Ordinals -- 1.2 Mappings and Fixpoints -- 1.3 Logic Programming -- 1.4 Semantics for Normal Logic Programs -- 1.4.1 Program Completion -- 1.4.2 Well-Founded Semantics -- 1.4.3 Stable Model Semantics -- 1.5 Probability Theory -- 1.6 Probabilistic Graphical Models -- 2: Probabilistic Logic Programming Languages -- 2.1 Languages with the Distribution Semantics -- 2.1.1 Logic Programs with Annotated Disjunctions -- 2.1.2 ProbLog -- 2.1.3 Probabilistic Horn Abduction -- 2.1.4 PRISM -- 2.2 The Distribution Semantics for Programs Without Function Symbols -- 2.3 Examples of Programs -- 2.4 Equivalence of Expressive Power -- 2.5 Translation to Bayesian Networks -- 2.6 Generality of the Distribution Semantics -- 2.7 Extensions of the Distribution Semantics -- 2.8 CP-Logic -- 2.9 Semantics for Non-Sound Programs -- 2.10 KBMC Probabilistic Logic Programming Languages -- 2.10.1 Bayesian Logic Programs -- 2.10.2 CLP(BN) -- 2.10.3 The Prolog Factor Language -- 2.11 Other Semantics for Probabilistic Logic Programming -- 2.11.1 Stochastic Logic Programs -- 2.11.2 ProPPR -- 2.12 Other Semantics for Probabilistic Logics -- 2.12.1 Nilsson's Probabilistic Logic -- 2.12.2 Markov Logic Networks -- 2.12.2.1 Encoding Markov Logic Networks with Probabilistic Logic Programming -- 2.12.3 Annotated Probabilistic Logic Programs -- 3: Semantics with Function Symbols -- 3.1 The Distribution Semantics for Programs with Function Symbols -- 3.2 Infinite Covering Set of Explanations -- 3.3 Comparison with Sato and Kameya's Definition -- 4: Semantics for Hybrid Programs.
,
4.1 Hybrid ProbLog -- 4.2 Distributional Clauses -- 4.3 Extended PRISM -- 4.4 cplint Hybrid Programs -- 4.5 Probabilistic Constraint Logic Programming -- 4.5.1 Dealing with Imprecise Probability Distributions -- 5: Exact Inference -- 5.1 PRISM -- 5.2 Knowledge Compilation -- 5.3 ProbLog1 -- 5.4 cplint -- 5.5 SLGAD -- 5.6 PITA -- 5.7 ProbLog2 -- 5.8 TP Compilation -- 5.9 Modeling Assumptions in PITA -- 5.9.1 PITA(OPT) -- 5.9.2 MPE with PITA -- 5.10 Inference for Queries with an Infinite Number of Explanations -- 5.11 Inference for Hybrid Programs -- 6: Lifted Inference -- 6.1 Preliminaries on Lifted Inference -- 6.1.1 Variable Elimination -- 6.1.2 GC-FOVE -- 6.2 LP2 -- 6.2.1 Translating ProbLog into PFL -- 6.3 Lifted Inference with Aggregation Parfactors -- 6.4 Weighted First-Order Model Counting -- 6.5 Cyclic Logic Programs -- 6.6 Comparison of the Approaches -- 7: Approximate Inference -- 7.1 ProbLog1 -- 7.1.1 Iterative Deepening -- 7.1.2 k-best -- 7.1.3 Monte Carlo -- 7.2 MCINTYRE -- 7.3 Approximate Inference for Queries with an Infinite Number of Explanations -- 7.4 Conditional Approximate Inference -- 7.5 Approximate Inference by Sampling for Hybrid Programs -- 7.6 Approximate Inference with Bounded Error for Hybrid Programs -- 7.7 k-Optimal -- 7.8 Explanation-Based Approximate Weighted Model Counting -- 7.9 Approximate Inference with TP-compilation -- 7.10 DISTR and EXP Tasks -- 8: Non-Standard Inference -- 8.1 Possibilistic Logic Programming -- 8.2 Decision-Theoretic ProbLog -- 8.3 Algebraic ProbLog -- 9: Parameter Learning -- 9.1 PRISM Parameter Learning -- 9.2 LLPAD and ALLPAD Parameter Learning -- 9.3 LeProbLog -- 9.4 EMBLEM -- 9.5 ProbLog2 Parameter Learning -- 9.6 Parameter Learning for Hybrid Programs -- 10: Structure Learning -- 10.1 Inductive Logic Programming -- 10.2 LLPAD and ALLPAD Structure Learning.
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10.3 ProbLog Theory Compression -- 10.4 ProbFOIL and ProbFOIL+ -- 10.5 SLIPCOVER -- 10.5.1 The Language Bias -- 10.5.2 Description of the Algorithm -- 10.5.2.1 Function INITIALBEAMS -- 10.5.2.2 Beam Search with Clause Refinements -- 10.5.3 Execution Example -- 10.6 Examples of Datasets -- 11: cplint Examples -- 11.1 cplint Commands -- 11.2 Natural Language Processing -- 11.2.1 Probabilistic Context-Free Grammars -- 11.2.2 Probabilistic Left Corner Grammars -- 11.2.3 Hidden Markov Models -- 11.3 Drawing Binary Decision Diagrams -- 11.4 Gaussian Processes -- 11.5 Dirichlet Processes -- 11.5.1 The Stick-Breaking Process -- 11.5.2 The Chinese Restaurant Process -- 11.5.3 Mixture Model -- 11.6 Bayesian Estimation -- 11.7 Kalman Filter -- 11.8 Stochastic Logic Programs -- 11.9 Tile Map Generation -- 11.10 Markov Logic Networks -- 11.11 Truel -- 11.12 Coupon Collector Problem -- 11.13 One-Dimensional Random Walk -- 11.14 Latent Dirichlet Allocation -- 11.15 The Indian GPA Problem -- 11.16 Bongard Problems -- 12: Conclusions -- References -- Index -- About the Author.
Weitere Ausg.:
ISBN 87-7022-018-2
Sprache:
Englisch
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