Probabilistic Reasoning In Expert Systems

Probabilistic Reasoning In Expert Systems Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Probabilistic Reasoning In Expert Systems book. This book definitely worth reading, it is an incredibly well-written.

Probabilistic Reasoning in Expert Systems

Richard E. Neapolitan
Probabilistic Reasoning in Expert Systems Book PDF

Author : Richard E. Neapolitan
Publisher : CreateSpace
Page : 448 pages
File Size : 43,9 Mb
Release : 2012-06-01
Category : Computers
ISBN : 1477452540

Get Book

Probabilistic Reasoning in Expert Systems by Richard E. Neapolitan Pdf

This text is a reprint of the seminal 1989 book Probabilistic Reasoning in Expert systems: Theory and Algorithms, which helped serve to create the field we now call Bayesian networks. It introduces the properties of Bayesian networks (called causal networks in the text), discusses algorithms for doing inference in Bayesian networks, covers abductive inference, and provides an introduction to decision analysis. Furthermore, it compares rule-base experts systems to ones based on Bayesian networks, and it introduces the frequentist and Bayesian approaches to probability. Finally, it provides a critique of the maximum entropy formalism. Probabilistic Reasoning in Expert Systems was written from the perspective of a mathematician with the emphasis being on the development of theorems and algorithms. Every effort was made to make the material accessible. There are ample examples throughout the text. This text is important reading for anyone interested in both the fundamentals of Bayesian networks and in the history of how they came to be. It also provides an insightful comparison of the two most prominent approaches to probability.

Probabilistic Reasoning in Expert Systems

Richard E. Neapolitan
Probabilistic Reasoning in Expert Systems Book PDF

Author : Richard E. Neapolitan
Publisher : Wiley-Interscience
Page : 492 pages
File Size : 50,8 Mb
Release : 1990-03-16
Category : Computers
ISBN : UOM:39015017023329

Get Book

Probabilistic Reasoning in Expert Systems by Richard E. Neapolitan Pdf

Addresses the use probability theory as a tool for designing with and implementing uncertainity reasoning. Provides many concrete algorithms, explores techniques for solving multimembership classification problems not based directly on causal networks, and offers practical recommendations, matching specific methods with sample expert systems.

Probabilistic Reasoning in Intelligent Systems

Judea Pearl
Probabilistic Reasoning in Intelligent Systems Book PDF

Author : Judea Pearl
Publisher : Elsevier
Page : 552 pages
File Size : 55,8 Mb
Release : 2014-06-28
Category : Computers
ISBN : 9780080514895

Get Book

Probabilistic Reasoning in Intelligent Systems by Judea Pearl Pdf

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 Networks and Expert Systems

Robert G. Cowell,Philip Dawid,Steffen L. Lauritzen,David J. Spiegelhalter
Probabilistic Networks and Expert Systems Book PDF

Author : Robert G. Cowell,Philip Dawid,Steffen L. Lauritzen,David J. Spiegelhalter
Publisher : Springer Science & Business Media
Page : 340 pages
File Size : 47,6 Mb
Release : 2007-07-16
Category : Computers
ISBN : 0387718230

Get Book

Probabilistic Networks and Expert Systems by Robert G. Cowell,Philip Dawid,Steffen L. Lauritzen,David J. Spiegelhalter Pdf

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 Expert Systems

Glenn Shafer
Probabilistic Expert Systems Book PDF

Author : Glenn Shafer
Publisher : SIAM
Page : 88 pages
File Size : 47,8 Mb
Release : 1996-01-01
Category : Computers
ISBN : 1611970040

Get Book

Probabilistic Expert Systems by Glenn Shafer Pdf

Probabilistic Expert Systems emphasizes the basic computational principles that make probabilistic reasoning feasible in expert systems. The key to computation in these systems is the modularity of the probabilistic model. Shafer describes and compares the principal architectures for exploiting this modularity in the computation of prior and posterior probabilities. He also indicates how these similar yet different architectures apply to a wide variety of other problems of recursive computation in applied mathematics and operations research. The field of probabilistic expert systems has continued to flourish since the author delivered his lectures on the topic in June 1992, but the understanding of join-tree architectures has remained missing from the literature. This monograph fills this void by providing an analysis of join-tree methods for the computation of prior and posterior probabilities in belief nets. These methods, pioneered in the mid to late 1980s, continue to be central to the theory and practice of probabilistic expert systems. In addition to purely probabilistic expert systems, join-tree methods are also used in expert systems based on Dempster-Shafer belief functions or on possibility measures. Variations are also used for computation in relational databases, in linear optimization, and in constraint satisfaction. This book describes probabilistic expert systems in a more rigorous and focused way than existing literature, and provides an annotated bibliography that includes pointers to conferences and software. Also included are exercises that will help the reader begin to explore the problem of generalizing from probability to broader domains of recursive computation.

Systematic Introduction to Expert Systems

Frank Puppe
Systematic Introduction to Expert Systems Book PDF

Author : Frank Puppe
Publisher : Springer Science & Business Media
Page : 353 pages
File Size : 53,8 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9783642779718

Get Book

Systematic Introduction to Expert Systems by Frank Puppe Pdf

At present one of the main obstacles to a broader application of expert systems is the lack of a theory to tell us which problem-solving methods areavailable for a given problem class. Such a theory could lead to significant progress in the following central aims of the expert system technique: - Evaluating the technical feasibility of expert system projects: This depends on whether there is a suitable problem-solving method, and if possible a corresponding tool, for the given problem class. - Simplifying knowledge acquisition and maintenance: The problem-solving methods provide direct assistance as interpretation models in knowledge acquisition. Also, they make possible the development of problem-specific expert system tools with graphical knowledge acquisition components, which can be used even by experts without programming experience. - Making use of expert systems as a knowledge medium: The structured knowledge in expert systems can be used not only for problem solving but also for knowledge communication and tutorial purposes. With such a theory in mind, this book provides a systematic introduction to expert systems. It describes the basic knowledge representations and the present situation with regard tothe identification, realization, and integration of problem-solving methods for the main problem classes of expert systems: classification (diagnostics), construction, and simulation.

Representing and Reasoning with Probabilistic Knowledge

Fahiem Bacchus
Representing and Reasoning with Probabilistic Knowledge Book PDF

Author : Fahiem Bacchus
Publisher : Cambridge, Mass. : MIT Press
Page : 264 pages
File Size : 53,9 Mb
Release : 1990
Category : Computers
ISBN : UOM:39015021630440

Get Book

Representing and Reasoning with Probabilistic Knowledge by Fahiem Bacchus Pdf

Probabilistic information has many uses in an intelligent system. This book explores logical formalisms for representing and reasoning with probabilistic information that will be of particular value to researchers in nonmonotonic reasoning, applications of probabilities, and knowledge representation. It demonstrates that probabilities are not limited to particular applications, like expert systems; they have an important role to play in the formal design and specification of intelligent systems in general. Fahiem Bacchus focuses on two distinct notions of probabilities: one propositional, involving degrees of belief, the other proportional, involving statistics. He constructs distinct logics with different semantics for each type of probability that are a significant advance in the formal tools available for representing and reasoning with probabilities. These logics can represent an extensive variety of qualitative assertions, eliminating requirements for exact point-valued probabilities, and they can represent firstshy;order logical information. The logics also have proof theories which give a formal specification for a class of reasoning that subsumes and integrates most of the probabilistic reasoning schemes so far developed in AI. Using the new logical tools to connect statistical with propositional probability, Bacchus also proposes a system of direct inference in which degrees of belief can be inferred from statistical knowledge and demonstrates how this mechanism can be applied to yield a powerful and intuitively satisfying system of defeasible or default reasoning. Fahiem Bacchus is Assistant Professor of Computer Science at the University of Waterloo, Ontario. Contents: Introduction. Propositional Probabilities. Statistical Probabilities. Combining Statistical and Propositional Probabilities Default Inferences from Statistical Knowledge.

Interactive Collaborative Information Systems

Robert Babuška,Frans C.A. Groen
Interactive Collaborative Information Systems Book PDF

Author : Robert Babuška,Frans C.A. Groen
Publisher : Springer
Page : 586 pages
File Size : 48,8 Mb
Release : 2010-03-22
Category : Technology & Engineering
ISBN : 9783642116889

Get Book

Interactive Collaborative Information Systems by Robert Babuška,Frans C.A. Groen Pdf

The increasing complexity of our world demands new perspectives on the role of technology in decision making. Human decision making has its li- tations in terms of information-processing capacity. We need new technology to cope with the increasingly complex and information-rich nature of our modern society. This is particularly true for critical environments such as crisis management and tra?c management, where humans need to engage in close collaborations with arti?cial systems to observe and understand the situation and respond in a sensible way. We believe that close collaborations between humans and arti?cial systems will become essential and that the importance of research into Interactive Collaborative Information Systems (ICIS) is self-evident. Developments in information and communication technology have ra- cally changed our working environments. The vast amount of information available nowadays and the wirelessly networked nature of our modern so- ety open up new opportunities to handle di?cult decision-making situations such as computer-supported situation assessment and distributed decision making. To make good use of these new possibilities, we need to update our traditional views on the role and capabilities of information systems. The aim of the Interactive Collaborative Information Systems project is to develop techniques that support humans in complex information en- ronments and that facilitate distributed decision-making capabilities. ICIS emphasizes the importance of building actor-agent communities: close c- laborations between human and arti?cial actors that highlight their comp- mentary capabilities, and in which task distribution is ?exible and adaptive.

Computational Learning and Probabilistic Reasoning

Alexander Gammerman
Computational Learning and Probabilistic Reasoning Book PDF

Author : Alexander Gammerman
Publisher : John Wiley & Sons
Page : 352 pages
File Size : 55,6 Mb
Release : 1996-08-06
Category : Computers
ISBN : UOM:39015037793497

Get Book

Computational Learning and Probabilistic Reasoning by Alexander Gammerman Pdf

Providing a unified coverage of the latest research and applications methods and techniques, this book is devoted to two interrelated techniques for solving some important problems in machine intelligence and pattern recognition, namely probabilistic reasoning and computational learning. The contributions in this volume describe and explore the current developments in computer science and theoretical statistics which provide computational probabilistic models for manipulating knowledge found in industrial and business data. These methods are very efficient for handling complex problems in medicine, commerce and finance. Part I covers Generalisation Principles and Learning and describes several new inductive principles and techniques used in computational learning. Part II describes Causation and Model Selection including the graphical probabilistic models that exploit the independence relationships presented in the graphs, and applications of Bayesian networks to multivariate statistical analysis. Part III includes case studies and descriptions of Bayesian Belief Networks and Hybrid Systems. Finally, Part IV on Decision-Making, Optimization and Classification describes some related theoretical work in the field of probabilistic reasoning. Statisticians, IT strategy planners, professionals and researchers with interests in learning, intelligent databases and pattern recognition and data processing for expert systems will find this book to be an invaluable resource. Real-life problems are used to demonstrate the practical and effective implementation of the relevant algorithms and techniques.

Mechatronics 2017

Tomáš Březina,Ryszard Jabłoński
Mechatronics 2017 Book PDF

Author : Tomáš Březina,Ryszard Jabłoński
Publisher : Springer
Page : 753 pages
File Size : 55,7 Mb
Release : 2017-08-16
Category : Technology & Engineering
ISBN : 9783319659602

Get Book

Mechatronics 2017 by Tomáš Březina,Ryszard Jabłoński Pdf

This book presents nearly 90 carefully selected contributions at the 12th International Conference Mechatronics, which took place in Brno, Czech Republic on 6–8 September 2017. Reflecting the most progressive and constantly changing areas of mechatronics, these proceedings includes papers concerning modeling and simulation, automatic control, robotics, sensors and actuators, electrical machines, and energy harvesting. It not only offers inspiration, but also deepens readers’ interdisciplinary and integrated understanding of modern engineering. The book is intended for experts in the integration of electronic, mechanical, control and computer sciences.

Probabilistic Similarity Networks

David E. Heckerman
Probabilistic Similarity Networks Book PDF

Author : David E. Heckerman
Publisher : MIT Press (MA)
Page : 272 pages
File Size : 49,6 Mb
Release : 1991
Category : Computers
ISBN : UOM:39015025008452

Get Book

Probabilistic Similarity Networks by David E. Heckerman Pdf

In this remarkable blend of formal theory and practical application, David Heckerman develops methods for building normative expert systems—expert systems that encode knowledge in a decision-theoretic framework. Heckerman introduces the similarity network and partition, two extensions to the influence diagram representation. He uses the new representations to construct Pathfinder, a large, normative expert system for the diagnosis of lymph-node diseases. Heckerman shows that such expert systems can be built efficiently, and that the use of a normative theory as the framework for representing knowledge can dramatically improve the quality of expertise that is delivered to the user. He concludes with a formal evaluation of the power of his methods for building normative expert systems. David Heckerman is Assistant Professor of Computer Science at the University of Southern California. He received his doctoral degree in Medical Information Sciences from Stanford University. Contents: Introduction. Similarity Networks and Partitions: A Simple Example. Theory of Similarity Networks. Pathfinder: A Case Study. An Evaluation of Pathfinder. Conclusions and Future Work.

Modeling and Reasoning with Bayesian Networks

Adnan Darwiche
Modeling and Reasoning with Bayesian Networks Book PDF

Author : Adnan Darwiche
Publisher : Cambridge University Press
Page : 561 pages
File Size : 42,9 Mb
Release : 2009-04-06
Category : Computers
ISBN : 9780521884389

Get Book

Modeling and Reasoning with Bayesian Networks by Adnan Darwiche Pdf

This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.

The Evidential Foundations of Probabilistic Reasoning

David A. Schum
The Evidential Foundations of Probabilistic Reasoning Book PDF

Author : David A. Schum
Publisher : Northwestern University Press
Page : 572 pages
File Size : 42,8 Mb
Release : 2001
Category : History
ISBN : 0810118211

Get Book

The Evidential Foundations of Probabilistic Reasoning by David A. Schum Pdf

In this work Schum develops a general theory of evidence as it is understood and applied across a broad range of disciplines and practical undertakings. He include insights from law, philosophy, logic, probability, semiotics, artificial intelligence, psychology and history.

Probabilistic Methods for Financial and Marketing Informatics

Richard E. Neapolitan,Xia Jiang
Probabilistic Methods for Financial and Marketing Informatics Book PDF

Author : Richard E. Neapolitan,Xia Jiang
Publisher : Elsevier
Page : 432 pages
File Size : 50,7 Mb
Release : 2010-07-26
Category : Business & Economics
ISBN : 9780080555676

Get Book

Probabilistic Methods for Financial and Marketing Informatics by Richard E. Neapolitan,Xia Jiang Pdf

Probabilistic Methods for Financial and Marketing Informatics aims to provide students with insights and a guide explaining how to apply probabilistic reasoning to business problems. Rather than dwelling on rigor, algorithms, and proofs of theorems, the authors concentrate on showing examples and using the software package Netica to represent and solve problems. The book contains unique coverage of probabilistic reasoning topics applied to business problems, including marketing, banking, operations management, and finance. It shares insights about when and why probabilistic methods can and cannot be used effectively. This book is recommended for all R&D professionals and students who are involved with industrial informatics, that is, applying the methodologies of computer science and engineering to business or industry information. This includes computer science and other professionals in the data management and data mining field whose interests are business and marketing information in general, and who want to apply AI and probabilistic methods to their problems in order to better predict how well a product or service will do in a particular market, for instance. Typical fields where this technology is used are in advertising, venture capital decision making, operational risk measurement in any industry, credit scoring, and investment science. Unique coverage of probabilistic reasoning topics applied to business problems, including marketing, banking, operations management, and finance Shares insights about when and why probabilistic methods can and cannot be used effectively Complete review of Bayesian networks and probabilistic methods for those IT professionals new to informatics.

Quantified Representation of Uncertainty and Imprecision

Dov M. Gabbay,Philippe Smets
Quantified Representation of Uncertainty and Imprecision Book PDF

Author : Dov M. Gabbay,Philippe Smets
Publisher : Springer Science & Business Media
Page : 496 pages
File Size : 53,7 Mb
Release : 1998-10-31
Category : Philosophy
ISBN : 0792351002

Get Book

Quantified Representation of Uncertainty and Imprecision by Dov M. Gabbay,Philippe Smets Pdf

We are happy to present the first volume of the Handbook of Defeasible Reasoning and Uncertainty Management Systems. Uncertainty pervades the real world and must therefore be addressed by every system that attempts to represent reality. The representation of uncertainty is a ma jor concern of philosophers, logicians, artificial intelligence researchers and com puter sciencists, psychologists, statisticians, economists and engineers. The present Handbook volumes provide frontline coverage of this area. This Handbook was produced in the style of previous handbook series like the Handbook of Philosoph ical Logic, the Handbook of Logic in Computer Science, the Handbook of Logic in Artificial Intelligence and Logic Programming, and can be seen as a companion to them in covering the wide applications of logic and reasoning. We hope it will answer the needs for adequate representations of uncertainty. This Handbook series grew out of the ESPRIT Basic Research Project DRUMS II, where the acronym is made out of the Handbook series title. This project was financially supported by the European Union and regroups 20 major European research teams working in the general domain of uncertainty. As a fringe benefit of the DRUMS project, the research community was able to create this Hand book series, relying on the DRUMS participants as the core of the authors for the Handbook together with external international experts.