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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 |
MARC
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| 100 | 1 | |a Riguzzi, Fabrizio, |e author. | |
| 245 | 1 | 0 | |a Foundations of probabilistic logic programming : |b languages, semantics, inference and learning / |c Fabrizio Riguzzi. |
| 264 | 1 | |a Gistrup, Denmark : |b River Publishers, |c [2018] | |
| 264 | 4 | |c ©2018 | |
| 300 | |a 1 online resource (xxxiii, 387 pages .) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a River Publishers series in software engineering | |
| 588 | 0 | |a Online resource; title from PDF title page (EBSCO, viewed November 13, 2018) | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 520 | |a 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 Programming. Logic enables the representation of complex relations among entities while probability theory is useful for model uncertainty over attributes and relations. Combining the two is a very active field of study. Probabilistic Programming extends programming languages with probabilistic primitives that can be used to write complex probabilistic models. Algorithms for the inference and learning tasks are then provided automatically by the system. Probabilistic Logic programming is at the same time a logic language, with its knowledge representation capabilities, and a Turing complete language, with its computation capabilities, thus providing the best of both worlds. Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of activity, with many proposals for languages and algorithms for inference and learning. Foundations of Probabilistic Logic Programming aims at providing an overview of the field with a special emphasis on languages under the Distribution Semantics, one of the most influential approaches. The book presents the main ideas for semantics, inference, and learning and highlights connections between the methods. Many examples of the book include a link to a page of the web application http://cplint.eu where the code can be run online. | ||
| 545 | 0 | |a Fabrizio Riguzzi | |
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Logic programming. | |
| 650 | 0 | |a Probabilities |x Data processing. | |
| 650 | 6 | |a Programmation logique. | |
| 650 | 6 | |a Probabilités |x Informatique. | |
| 650 | 7 | |a COMPUTERS |x Logic Design. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS / Programming / Software Development |2 bisacsh | |
| 650 | 7 | |a SCIENCE / Energy |2 bisacsh | |
| 650 | 7 | |a Logic programming. |2 fast |0 (OCoLC)fst01002056 | |
| 650 | 7 | |a Probabilities |x Data processing. |2 fast |0 (OCoLC)fst01077741 | |
| 776 | 0 | 8 | |i Print version: |a Riguzzi, Fabrizio. |t Foundations of probabilistic logic programming. |d Gistrup, Denmark : River Publishers, [2018] |z 8770220182 |z 9788770220187 |w (OCoLC)1045185974 |
| 830 | 0 | |a River Publishers series in software engineering. | |
| 856 | 4 | 0 | |u https://ebsco.uam.elogim.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1917713 |z Texto completo |
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