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Foundations of probabilistic logic programming : languages, semantics, inference and learning /
Probabilistic Logic Programming extends Logic Programming by enabling the representation of uncertain information by means of probability theory. Probabilistic Logic Programming is at the intersection of two wider research fields: the integration of logic and probability and Probabilistic Programmin...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Gistrup, Denmark :
River Publishers,
[2018]
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| Colección: | River Publishers series in software engineering.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Foreword xi
- Preface xiii
- Acknowledgements xv
- List of Figures xvii
- List of Tables xxi
- List of Examples xxiii
- List of Definitions xxvii
- List of Theorems xxix
- List of Abbreviations xxxi
- 1 Preliminaries 1
- 1.1 Orders, Lattices, Ordinals 1
- 1.2 Mappings and Fixpoints 3
- 1.3 Logic Programming 4
- 1.4 Semantics for Normal Logic Programs 13
- 1.4.1 Program Completion 13
- 1.4.2 Well-Founded Semantics 15
- 1.4.3 Stable Model Semantics 21
- 1.5 Probability Theory 23
- 1.6 Probabilistic Graphical Models 32
- 2 Probabilistic Logic Programming Languages 41
- 2.1 Languages with the Distribution Semantics 41
- 2.1.1 Logic Programs with Annotated Disjunctions 42
- 2.1.2 ProbLog 43
- 2.1.3 Probabilistic Horn Abduction 43
- 2.1.4 PRISM 44
- 2.2 The Distribution Semantics for Programs Without Function Symbols 45
- 2.3 Examples of Programs 50
- 2.4 Equivalence of Expressive Power 56
- 2.5 Translation to Bayesian Networks 58
- 2.6 Generality of the Distribution Semantics 62
- 2.7 Extensions of the Distribution Semantics 64
- 2.8 CP-Logic 66
- 2.9 Semantics for Non-Sound Programs 71
- 2.10 KBMC Probabilistic Logic Programming Languages 76
- 2.10.1 Bayesian Logic Programs 76
- 2.10.2 CLP(BN) 76
- 2.10.3 The Prolog Factor Language 79
- 2.11 Other Semantics for Probabilistic Logic Programming 80
- 2.11.1 Stochastic Logic Programs 81
- 2.11.2 ProPPR 82
- 2.12 Other Semantics for Probabilistic Logics 84
- 2.12.1 Nilsson's Probabilistic Logic 84
- 2.12.2 Markov Logic Networks 84
- 2.12.2.1 Encoding Markov Logic Networks with Probabilistic Logic Programming 85
- 2.12.3 Annotated Probabilistic Logic Programs 88
- 3 Semantics with Function Symbols 91
- 3.1 The Distribution Semantics for Programs with Function Symbols 92
- 3.2 Infinite Covering Set of Explanations 97
- 3.3 Comparison with Sato and Kameya's Definition 110
- 4 Semantics for Hybrid Programs 115
- 4.1 Hybrid ProbLog 115
- 4.2 Distributional Clauses 118
- 4.3 Extended PRISM 124.
- 4.4 cplint Hybrid Programs 126
- 4.5 Probabilistic Constraint Logic Programming 130
- 4.5.1 Dealing with Imprecise Probability Distributions 135
- 5 Exact Inference 145
- 5.1 PRISM 146
- 5.2 Knowledge Compilation 150
- 5.3 ProbLog1
- 151
- 5.4 cplint 155
- 5.5 SLGAD 157
- 5.6 PITA 158
- 5.7 ProbLog2
- 163
- 5.8 TP Compilation 176
- 5.9 Modeling Assumptions in PITA 178
- 5.9.1 PITA(OPT) 181
- 5.9.2 MPE with PITA 186
- 5.10 Inference for Queries with an Infinite Number of Explanations 186
- 5.11 Inference for Hybrid Programs 187
- 6 Lifted Inference 195
- 6.1 Preliminaries on Lifted Inference 195
- 6.1.1 Variable Elimination 197
- 6.1.2 GC-FOVE 201
- 6.2 LP2
- 202
- 6.2.1 Translating ProbLog into PFL 202
- 6.3 Lifted Inference with Aggregation Parfactors 205
- 6.4 Weighted First-Order Model Counting 207
- 6.5 Cyclic Logic Programs 210
- 6.6 Comparison of the Approaches 210
- 7 Approximate Inference 213
- 7.1 ProbLog1
- 213
- 7.1.1 Iterative Deepening 213
- 7.1.2 k-best 215
- 7.1.3 Monte Carlo 216
- 7.2 MCINTYRE 218
- 7.3 Approximate Inference for Queries with an Infinite Number of Explanations 221
- 7.4 Conditional Approximate Inference 222
- 7.5 Approximate Inference by Sampling for Hybrid Programs 223
- 7.6 Approximate Inference with Bounded Error for Hybrid Programs 226
- 7.7 k-Optimal 229
- 7.8 Explanation-Based Approximate Weighted Model Counting 231
- 7.9 Approximate Inference with TP -compilation 233
- 7.10 DISTR and EXP Tasks 234
- 8 Non-Standard Inference 239
- 8.1 Possibilistic Logic Programming 239
- 8.2 Decision-Theoretic ProbLog 241
- 8.3 Algebraic ProbLog 250
- 9 Parameter Learning 259
- 9.1 PRISM Parameter Learning 259
- 9.2 LLPAD and ALLPAD Parameter Learning 265
- 9.3 LeProbLog 267
- 9.4 EMBLEM 270
- 9.5 ProbLog2 Parameter Learning 280
- 9.6 Parameter Learning for Hybrid Programs 282
- 10 Structure Learning 283
- 10.1 Inductive Logic Programming 283
- 10.2 LLPAD and ALLPAD Structure Learning 287.
- 10.3 ProbLog Theory Compression 289
- 10.4 ProbFOIL and ProbFOIL+ 290
- 10.5 SLIPCOVER 296
- 10.5.1 The Language Bias 296
- 10.5.2 Description of the Algorithm 296
- 10.5.2.1 Function INITIALBEAMS 298
- 10.5.2.2 Beam Search with Clause Refinements 300
- 10.5.3 Execution Example 301
- 10.6 Examples of Datasets 304
- 11 cplint Examples 305
- 11.1 cplint Commands 305
- 11.2 Natural Language Processing 309
- 11.2.1 Probabilistic Context-Free Grammars 309
- 11.2.2 Probabilistic Left Corner Grammars 310
- 11.2.3 Hidden Markov Models 311
- 11.3 Drawing Binary Decision Diagrams 313
- 11.4 Gaussian Processes 314
- 11.5 Dirichlet Processes 318
- 11.5.1 The Stick-Breaking Process 319
- 11.5.2 The Chinese Restaurant Process 322
- 11.5.3 Mixture Model 324
- 11.6 Bayesian Estimation 326
- 11.7 Kalman Filter 327
- 11.8 Stochastic Logic Programs 330
- 11.9 Tile Map Generation 332
- 11.10 Markov Logic Networks 334
- 11.11 Truel 335
- 11.12 Coupon Collector Problem 339
- 11.13 One-Dimensional Random Walk 341
- 11.14 Latent Dirichlet Allocation 342
- 11.15 The Indian GPA Problem 346
- 11.16 Bongard Problems 348
- 12 Conclusions 351
- References 353
- Index 375
- About the Author 387.