EXPERT SYSTEMS AND PROBABILISTIC NETWORK MODELS MONOGRAPHS IN COMPUTER SCIENCE

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Expert Systems And Probabilistic Network Models

Author : Enrique Castillo
ISBN : 9781461222705
Genre : Computers
File Size : 63.47 MB
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Artificial intelligence and expert systems have seen a great deal of research in recent years, much of which has been devoted to methods for incorporating uncertainty into models. This book is devoted to providing a thorough and up-to-date survey of this field for researchers and students.
Category: Computers

Advances In Bayesian Networks

Author : José A. Gámez
ISBN : 9783540398790
Genre : Mathematics
File Size : 75.47 MB
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In recent years probabilistic graphical models, especially Bayesian networks and decision graphs, have experienced significant theoretical development within areas such as artificial intelligence and statistics. This carefully edited monograph is a compendium of the most recent advances in the area of probabilistic graphical models such as decision graphs, learning from data and inference. It presents a survey of the state of the art of specific topics of recent interest of Bayesian Networks, including approximate propagation, abductive inferences, decision graphs, and applications of influence. In addition, Advances in Bayesian Networks presents a careful selection of applications of probabilistic graphical models to various fields such as speech recognition, meteorology or information retrieval.
Category: Mathematics

Resilience Engineering

Author : Nii Attoh-Okine
ISBN : 9780521193498
Genre : Mathematics
File Size : 89.57 MB
Format : PDF
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The book is intended for readers who have backgrounds in probability. It is suitable for practicing engineers, analysts, and researchers.
Category: Mathematics

Bayesian Networks And Influence Diagrams A Guide To Construction And Analysis

Author : Uffe B. Kjærulff
ISBN : 9781461451044
Genre : Computers
File Size : 56.37 MB
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Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition, provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. This new edition contains six new sections, in addition to fully-updated examples, tables, figures, and a revised appendix. Intended primarily for practitioners, this book does not require sophisticated mathematical skills or deep understanding of the underlying theory and methods nor does it discuss alternative technologies for reasoning under uncertainty. The theory and methods presented are illustrated through more than 140 examples, and exercises are included for the reader to check his or her level of understanding. The techniques and methods presented for knowledge elicitation, model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been developed and refined on the basis of numerous courses that the authors have held for practitioners worldwide.
Category: Computers

Advances In Case Based Reasoning

Author : Enrico Blanzieri
ISBN : UOM:39015048228681
Genre : Expert systems (Computer science)
File Size : 72.79 MB
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This book constitutes the refereed proceedings of the 5th European Workshop on Case-Based Reasonning, EWCBR 2000, held in Trento, Italy in September 2000. The 40 revised full papers presented together with two invited contributions were carefully reviewed and selected for inclusion in the book. All curves issues in case-based reasoning, ranging from foundational and theoretical aspects to advanced applications in various fields are addressed.
Category: Expert systems (Computer science)

Uncertainty In Artificial Intelligence

Author : David Heckerman
ISBN : 9781483214511
Genre : Computers
File Size : 60.36 MB
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Uncertainty in Artificial Intelligence contains the proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence held at the Catholic University of America in Washington, DC, on July 9-11, 1993. The papers focus on methods of reasoning and decision making under uncertainty as applied to problems in artificial intelligence (AI) and cover topics ranging from knowledge acquisition and automated model construction to learning, planning, temporal reasoning, and machine vision. Comprised of 66 chapters, this book begins with a discussion on causality in Bayesian belief networks before turning to a decision theoretic account of conditional ought statements that rectifies glaring deficiencies in classical deontic logic and forms a sound basis for qualitative decision theory. Subsequent chapters explore trade-offs in constructing and evaluating temporal influence diagrams; normative engineering risk management systems; additive belief-network models; and sensitivity analysis for probability assessments in Bayesian networks. Automated model construction and learning as well as algorithms for inference and decision making are also considered. This monograph will be of interest to both students and practitioners in the fields of AI and computer science.
Category: Computers