
Foundations of Probabilistic Logic Programming
Description
Alles über E-Books | Antworten auf Fragen rund um E-Books, Kopierschutz und Dateiformate finden Sie in unserem Info- & Hilfebereich.
More details
Other editions
Additional editions

Content
- Front Cover
- Half Title Page
- RIVER PUBLISHERS SERIES IN SOFTWAREENGINEERING
- Title Page
- Copyright Page
- Contents
- Foreword
- Preface
- Acknowledgments
- List of Figures
- List of Tables
- List of Examples
- List of Definitions
- List of Theorems
- List of Abbreviations
- Chapter 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
- Probabilistic Graphical Models
- Chapter 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
- 12.2.2 Markov Logic Networks
- 12.2.2.1 Encoding Markov Logic Networks with Probabilistic Logic Programming
- 12.3 Annotated Probabilistic Logic Programs
- Chapter 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
- Chapter 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
- Chapter 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
- Modeling Assumptions in PITA
- PITA(OPT)
- MPE with PITA
- Inference for Queries with an Infinite Number of Explanations
- Inference for Hybrid Programs
- Chapter 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
- Chapter 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
- DISTR and EXP Tasks
- Chapter 8 - Non-Standard Inference
- 8.1 Possibilistic Logic Programming
- 8.2 Decision-Theoretic ProbLog
- 8.3 Algebraic ProbLog
- Chapter 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
- Chapter 10 - Structure Learning
- 10.1 Inductive Logic Programming
- 10.2 LLPAD and ALLPAD Structure Learning
- 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
- Examples of Datasets
- Chapter 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
- The Stick-Breaking Process
- The Chinese Restaurant Process
- 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
- Bongard Problems
- Chapter 12 - Conclusions
- References
- Index
- About the Author
- Back Cover
System requirements
File format: PDF
Copy-Protection: Adobe-DRM (Digital Rights Management)
System requirements:
- Computer (Windows; MacOS X; Linux): Install the free reader Adobe Digital Editions prior to download (see eBook Help).
- Tablet/smartphone (Android; iOS): Install the free app Adobe Digital Editions or the app PocketBook before downloading (see eBook Help).
- E-reader: Bookeen, Kobo, Pocketbook, Sony, Tolino and many more (only limited: Kindle).
The file format PDF always displays a book page identically on any hardware. This makes PDF suitable for complex layouts such as those used in textbooks and reference books (images, tables, columns, footnotes). Unfortunately, on the small screens of e-readers or smartphones, PDFs are rather annoying, requiring too much scrolling.
This eBook uses Adobe-DRM, a „hard” copy protection. If the necessary requirements are not met, unfortunately you will not be able to open the eBook. You will therefore need to prepare your reading hardware before downloading.
Please note: We strongly recommend that you authorise using your personal Adobe ID after installation of any reading software.
For more information, see our eBook Help page.