Probabilistic Logics And Probabilistic Networks

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Probabilistic Logics and Probabilistic Networks

Author : Rolf Haenni
Publisher : Springer Science & Business Media
ISBN 13 : 9400700083
Total Pages : 155 pages
Book Rating : 4.4/5 (7 download)

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Book Synopsis Probabilistic Logics and Probabilistic Networks by : Rolf Haenni

Download or read book Probabilistic Logics and Probabilistic Networks written by Rolf Haenni and published by Springer Science & Business Media. This book was released on 2010-11-19 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt: While probabilistic logics in principle might be applied to solve a range of problems, in practice they are rarely applied - perhaps because they seem disparate, complicated, and computationally intractable. This programmatic book argues that several approaches to probabilistic logic fit into a simple unifying framework in which logically complex evidence is used to associate probability intervals or probabilities with sentences. Specifically, Part I shows that there is a natural way to present a question posed in probabilistic logic, and that various inferential procedures provide semantics for that question, while Part II shows that there is the potential to develop computationally feasible methods to mesh with this framework. The book is intended for researchers in philosophy, logic, computer science and statistics. A familiarity with mathematical concepts and notation is presumed, but no advanced knowledge of logic or probability theory is required.

Probabilistic Logic Networks

Author : Ben Goertzel
Publisher : Springer Science & Business Media
ISBN 13 : 0387768726
Total Pages : 331 pages
Book Rating : 4.3/5 (877 download)

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Book Synopsis Probabilistic Logic Networks by : Ben Goertzel

Download or read book Probabilistic Logic Networks written by Ben Goertzel and published by Springer Science & Business Media. This book was released on 2008-12-16 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract In this chapter we provide an overview of probabilistic logic networks (PLN), including our motivations for developing PLN and the guiding principles underlying PLN. We discuss foundational choices we made, introduce PLN knowledge representation, and briefly introduce inference rules and truth-values. We also place PLN in context with other approaches to uncertain inference. 1.1 Motivations This book presents Probabilistic Logic Networks (PLN), a systematic and pragmatic framework for computationally carrying out uncertain reasoning – r- soning about uncertain data, and/or reasoning involving uncertain conclusions. We begin with a few comments about why we believe this is such an interesting and important domain of investigation. First of all, we hold to a philosophical perspective in which “reasoning” – properly understood – plays a central role in cognitive activity. We realize that other perspectives exist; in particular, logical reasoning is sometimes construed as a special kind of cognition that humans carry out only occasionally, as a deviation from their usual (intuitive, emotional, pragmatic, sensorimotor, etc.) modes of thought. However, we consider this alternative view to be valid only according to a very limited definition of “logic.” Construed properly, we suggest, logical reasoning may be understood as the basic framework underlying all forms of cognition, including those conventionally thought of as illogical and irrational.

Probabilistic Networks and Expert Systems

Author : Robert G. Cowell
Publisher : Springer Science & Business Media
ISBN 13 : 9780387718231
Total Pages : 340 pages
Book Rating : 4.7/5 (182 download)

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Book Synopsis Probabilistic Networks and Expert Systems by : Robert G. Cowell

Download or read book Probabilistic Networks and Expert Systems written by Robert G. Cowell and published by Springer Science & Business Media. This book was released on 2007-07-16 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic expert systems are graphical networks which support the modeling of uncertainty and decisions in large complex domains, while retaining ease of calculation. Building on original research by the authors, this book gives a thorough and rigorous mathematical treatment of the underlying ideas, structures, and algorithms. The book will be of interest to researchers in both artificial intelligence and statistics, who desire an introduction to this fascinating and rapidly developing field. The book, winner of the DeGroot Prize 2002, the only book prize in the field of statistics, is new in paperback.

Probabilistic Reasoning in Intelligent Systems

Author : Judea Pearl
Publisher : Elsevier
ISBN 13 : 0080514898
Total Pages : 552 pages
Book Rating : 4.0/5 (85 download)

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Book Synopsis Probabilistic Reasoning in Intelligent Systems by : Judea Pearl

Download or read book Probabilistic Reasoning in Intelligent Systems written by Judea Pearl and published by Elsevier. This book was released on 2014-06-28 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertainty--and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition--in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information. Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Probabilistic Inductive Logic Programming

Author : Luc De Raedt
Publisher : Springer
ISBN 13 : 354078652X
Total Pages : 341 pages
Book Rating : 4.5/5 (47 download)

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Book Synopsis Probabilistic Inductive Logic Programming by : Luc De Raedt

Download or read book Probabilistic Inductive Logic Programming written by Luc De Raedt and published by Springer. This book was released on 2008-02-26 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to probabilistic inductive logic programming. It places emphasis on the methods based on logic programming principles and covers formalisms and systems, implementations and applications, as well as theory.

Probabilistic Reasoning in Intelligent Systems

Author : Judea Pearl
Publisher : Morgan Kaufmann
ISBN 13 :
Total Pages : 572 pages
Book Rating : 4.:/5 (319 download)

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Book Synopsis Probabilistic Reasoning in Intelligent Systems by : Judea Pearl

Download or read book Probabilistic Reasoning in Intelligent Systems written by Judea Pearl and published by Morgan Kaufmann. This book was released on 1988 with total page 572 pages. Available in PDF, EPUB and Kindle. Book excerpt: Textbook offers an accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. For graduate-level courses in AI, operations research, and applied probability. Annotation copyright Book News, Inc. Portland, Or.

Sentential Probability Logic

Author : Theodore Hailperin
Publisher : Lehigh University Press
ISBN 13 : 9780934223454
Total Pages : 316 pages
Book Rating : 4.2/5 (234 download)

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Book Synopsis Sentential Probability Logic by : Theodore Hailperin

Download or read book Sentential Probability Logic written by Theodore Hailperin and published by Lehigh University Press. This book was released on 1996 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study presents a logic in which probability values play a semantic role comparable to that of truth values in conventional logic. The difference comes in with the semantic definition of logical consequence. It will be of interest to logicians, both philosophical and mathematical, and to investigators making use of logical inference under uncertainty, such as in operations research, risk analysis, artificial intelligence, and expert systems.

Probabilistic Foundations of Statistical Network Analysis

Author : Harry Crane
Publisher : CRC Press
ISBN 13 : 1351807331
Total Pages : 236 pages
Book Rating : 4.3/5 (518 download)

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Book Synopsis Probabilistic Foundations of Statistical Network Analysis by : Harry Crane

Download or read book Probabilistic Foundations of Statistical Network Analysis written by Harry Crane and published by CRC Press. This book was released on 2018-04-17 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic Foundations of Statistical Network Analysis presents a fresh and insightful perspective on the fundamental tenets and major challenges of modern network analysis. Its lucid exposition provides necessary background for understanding the essential ideas behind exchangeable and dynamic network models, network sampling, and network statistics such as sparsity and power law, all of which play a central role in contemporary data science and machine learning applications. The book rewards readers with a clear and intuitive understanding of the subtle interplay between basic principles of statistical inference, empirical properties of network data, and technical concepts from probability theory. Its mathematically rigorous, yet non-technical, exposition makes the book accessible to professional data scientists, statisticians, and computer scientists as well as practitioners and researchers in substantive fields. Newcomers and non-quantitative researchers will find its conceptual approach invaluable for developing intuition about technical ideas from statistics and probability, while experts and graduate students will find the book a handy reference for a wide range of new topics, including edge exchangeability, relative exchangeability, graphon and graphex models, and graph-valued Levy process and rewiring models for dynamic networks. The author’s incisive commentary supplements these core concepts, challenging the reader to push beyond the current limitations of this emerging discipline. With an approachable exposition and more than 50 open research problems and exercises with solutions, this book is ideal for advanced undergraduate and graduate students interested in modern network analysis, data science, machine learning, and statistics. Harry Crane is Associate Professor and Co-Director of the Graduate Program in Statistics and Biostatistics and an Associate Member of the Graduate Faculty in Philosophy at Rutgers University. Professor Crane’s research interests cover a range of mathematical and applied topics in network science, probability theory, statistical inference, and mathematical logic. In addition to his technical work on edge and relational exchangeability, relative exchangeability, and graph-valued Markov processes, Prof. Crane’s methods have been applied to domain-specific cybersecurity and counterterrorism problems at the Foreign Policy Research Institute and RAND’s Project AIR FORCE.

Probabilistic Boolean Networks

Author : Ilya Shmulevich
Publisher : SIAM
ISBN 13 : 0898716926
Total Pages : 276 pages
Book Rating : 4.8/5 (987 download)

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Book Synopsis Probabilistic Boolean Networks by : Ilya Shmulevich

Download or read book Probabilistic Boolean Networks written by Ilya Shmulevich and published by SIAM. This book was released on 2010-01-21 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first comprehensive treatment of probabilistic Boolean networks, unifying different strands of current research and addressing emerging issues.

Logic with a Probability Semantics

Author : Theodore Hailperin
Publisher : Rowman & Littlefield
ISBN 13 : 1611460107
Total Pages : 124 pages
Book Rating : 4.6/5 (114 download)

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Book Synopsis Logic with a Probability Semantics by : Theodore Hailperin

Download or read book Logic with a Probability Semantics written by Theodore Hailperin and published by Rowman & Littlefield. This book was released on 2011 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: The present study is an extension of the topic introduced in Dr. Hailperin's Sentential Probability Logic, where the usual true-false semantics for logic is replaced with one based more on probability, and where values ranging from 0 to 1 are subject to probability axioms. Moreover, as the word "sentential" in the title of that work indicates, the language there under consideration was limited to sentences constructed from atomic (not inner logical components) sentences, by use of sentential connectives ("no," "and," "or," etc.) but not including quantifiers ("for all," "there is"). An initial introduction presents an overview of the book. In chapter one, Halperin presents a summary of results from his earlier book, some of which extends into this work. It also contains a novel treatment of the problem of combining evidence: how does one combine two items of interest for a conclusion-each of which separately impart a probability for the conclusion-so as to have a probability for the conclusion basedon taking both of the two items of interest as evidence? Chapter two enlarges the Probability Logic from the first chapter in two respects: the language now includes quantifiers ("for all," and "there is") whose variables range over atomic sentences, notentities as with standard quantifier logic. (Hence its designation: ontological neutral logic.) A set of axioms for this logic is presented. A new sentential notion-the suppositional-in essence due to Thomas Bayes, is adjoined to this logic that later becomes the basis for creating a conditional probability logic. Chapter three opens with a set of four postulates for probability on ontologically neutral quantifier language. Many properties are derived and a fundamental theorem is proved, namely, for anyprobability model (assignment of probability values to all atomic sentences of the language) there will be a unique extension of the probability values to all closed sentences of the language. The chapter concludes by showing the Borel's early denumerableprobability concept (1909) can be justified by its being, in essence, close to Hailperin's probability result applied to denumerable language. The final chapter introduces the notion of conditional-probability to a language having quantifiers of the kind

Real-World Reasoning: Toward Scalable, Uncertain Spatiotemporal, Contextual and Causal Inference

Author : Ben Goertzel
Publisher : Springer Science & Business Media
ISBN 13 : 9491216112
Total Pages : 269 pages
Book Rating : 4.4/5 (912 download)

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Book Synopsis Real-World Reasoning: Toward Scalable, Uncertain Spatiotemporal, Contextual and Causal Inference by : Ben Goertzel

Download or read book Real-World Reasoning: Toward Scalable, Uncertain Spatiotemporal, Contextual and Causal Inference written by Ben Goertzel and published by Springer Science & Business Media. This book was released on 2011-12-02 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: The general problem addressed in this book is a large and important one: how to usefully deal with huge storehouses of complex information about real-world situations. Every one of the major modes of interacting with such storehouses – querying, data mining, data analysis – is addressed by current technologies only in very limited and unsatisfactory ways. The impact of a solution to this problem would be huge and pervasive, as the domains of human pursuit to which such storehouses are acutely relevant is numerous and rapidly growing. Finally, we give a more detailed treatment of one potential solution with this class, based on our prior work with the Probabilistic Logic Networks (PLN) formalism. We show how PLN can be used to carry out realworld reasoning, by means of a number of practical examples of reasoning regarding human activities inreal-world situations.

Foundations of Probabilistic Logic Programming

Author : Fabrizio Riguzzi
Publisher : CRC Press
ISBN 13 : 100079587X
Total Pages : 422 pages
Book Rating : 4.0/5 (7 download)

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Book Synopsis Foundations of Probabilistic Logic Programming by : Fabrizio Riguzzi

Download or read book Foundations of Probabilistic Logic Programming written by Fabrizio Riguzzi and published by CRC Press. This book was released on 2022-09-01 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science

Author : Franco Taroni
Publisher : John Wiley & Sons
ISBN 13 : 0470979739
Total Pages : 472 pages
Book Rating : 4.4/5 (79 download)

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Book Synopsis Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science by : Franco Taroni

Download or read book Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science written by Franco Taroni and published by John Wiley & Sons. This book was released on 2014-09-22 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Networks “This book should have a place on the bookshelf of every forensic scientist who cares about the science of evidence interpretation.” Dr. Ian Evett, Principal Forensic Services Ltd, London, UK Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science Second Edition Continuing developments in science and technology mean that the amounts of information forensic scientists are able to provide for criminal investigations is ever increasing. The commensurate increase in complexity creates diffculties for scientists and lawyers with regard to evaluation and interpretation, notably with respect to issues of inference and decision. Probability theory, implemented through graphical methods, and specifically Bayesian networks, provides powerful methods to deal with this complexity. Extensions of these methods to elements of decision theory provide further support and assistance to the judicial system. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science provides a unique and comprehensive introduction to the use of Bayesian decision networks for the evaluation and interpretation of scientific findings in forensic science, and for the support of decision-makers in their scientific and legal tasks. Includes self-contained introductions to probability and decision theory. Develops the characteristics of Bayesian networks, object-oriented Bayesian networks and their extension to decision models. Features implementation of the methodology with reference to commercial and academically available software. Presents standard networks and their extensions that can be easily implemented and that can assist in the reader’s own analysis of real cases. Provides a technique for structuring problems and organizing data based on methods and principles of scientific reasoning. Contains a method for the construction of coherent and defensible arguments for the analysis and evaluation of scientific findings and for decisions based on them. Is written in a lucid style, suitable for forensic scientists and lawyers with minimal mathematical background. Includes a foreword by Ian Evett. The clear and accessible style of this second edition makes this book ideal for all forensic scientists, applied statisticians and graduate students wishing to evaluate forensic findings from the perspective of probability and decision analysis. It will also appeal to lawyers and other scientists and professionals interested in the evaluation and interpretation of forensic findings, including decision making based on scientific information.

Philosophy of Probability

Author : J.P. Dubucs
Publisher : Springer Science & Business Media
ISBN 13 : 9401582084
Total Pages : 304 pages
Book Rating : 4.4/5 (15 download)

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Book Synopsis Philosophy of Probability by : J.P. Dubucs

Download or read book Philosophy of Probability written by J.P. Dubucs and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Philosophy of Probability provides a comprehensive introduction to theoretical issues that occupy a central position in disciplines ranging from philosophy of mind and epistemology to cognitive science, decision theory and artificial intelligence. Some contributions shed new light on the standard conceptions of probability (Bayesianism, logical and computational theories); others offer detailed analyses of two important topics in the field of cognitive science: the meaning and the representation of (partial) belief, and the management of uncertainty. The authors of this well-balanced account are philosophers as well as computer scientists (among them, L.J. Cohen, D. Miller, P. Gärdenfors, J. Vickers, D. Dubois and H. Prade). This multidisciplinary approach to probability is designed to illuminate the intricacies of the problems in the domain of cognitive inquiry. No one interested in epistemology or aritificial intelligence will want to miss it.

Statistical Relational Artificial Intelligence

Author : Luc De Kang
Publisher : Springer Nature
ISBN 13 : 3031015746
Total Pages : 175 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Statistical Relational Artificial Intelligence by : Luc De Kang

Download or read book Statistical Relational Artificial Intelligence written by Luc De Kang and published by Springer Nature. This book was released on 2022-05-31 with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt: An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.

Foundations of Probabilistic Programming

Author : Gilles Barthe
Publisher : Cambridge University Press
ISBN 13 : 110848851X
Total Pages : 583 pages
Book Rating : 4.1/5 (84 download)

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Book Synopsis Foundations of Probabilistic Programming by : Gilles Barthe

Download or read book Foundations of Probabilistic Programming written by Gilles Barthe and published by Cambridge University Press. This book was released on 2020-12-03 with total page 583 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an overview of the theoretical underpinnings of modern probabilistic programming and presents applications in e.g., machine learning, security, and approximate computing. Comprehensive survey chapters make the material accessible to graduate students and non-experts. This title is also available as Open Access on Cambridge Core.

Column Generation

Author : Guy Desaulniers
Publisher : Springer Science & Business Media
ISBN 13 : 0387254862
Total Pages : 369 pages
Book Rating : 4.3/5 (872 download)

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Book Synopsis Column Generation by : Guy Desaulniers

Download or read book Column Generation written by Guy Desaulniers and published by Springer Science & Business Media. This book was released on 2006-03-20 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: Column Generation is an insightful overview of the state of the art in integer programming column generation and its many applications. The volume begins with "A Primer in Column Generation" which outlines the theory and ideas necessary to solve large-scale practical problems, illustrated with a variety of examples. Other chapters follow this introduction on "Shortest Path Problems with Resource Constraints," "Vehicle Routing Problem with Time Window," "Branch-and-Price Heuristics," "Cutting Stock Problems," each dealing with methodological aspects of the field. Three chapters deal with transportation applications: "Large-scale Models in the Airline Industry," "Robust Inventory Ship Routing by Column Generation," and "Ship Scheduling with Recurring Visits and Visit Separation Requirements." Production is the focus of another three chapters: "Combining Column Generation and Lagrangian Relaxation," "Dantzig-Wolfe Decomposition for Job Shop Scheduling," and "Applying Column Generation to Machine Scheduling." The final chapter by François Vanderbeck, "Implementing Mixed Integer Column Generation," reviews how to set-up the Dantzig-Wolfe reformulation, adapt standard MIP techniques to the column generation context (branching, preprocessing, primal heuristics), and deal with specific column generation issues (initialization, stabilization, column management strategies).