%{search_type} search results

12 catalog results

RSS feed for this result
Book
1 online resource.
  • Fundamentals
  • Introduction
  • Networks
  • Probabilities
  • Probabilistic Networks
  • Solving Probabilistic Networks
  • Model Construction
  • Eliciting the Model
  • Modeling Techniques
  • Data-Driven Modeling
  • Model Analysis
  • Conflict Analysis
  • Sensitivity Analysis
  • Value of Information Analysis.
dx.doi.org SpringerLink
eReserve
MS&E-355-01
Book
xix, 464 p : ill. ; 26 cm.
  • 1. Introduction to probabilities, graphs, and causal models-- 2. A theory of inferred causation-- 3. Causal diagrams and the identification of causal effects-- 4. Actions, plans, and direct effects-- 5. Causality and structural models in the social sciences-- 6. Simpson's paradox, confounding, and collapsibility-- 7. Structural and counterfactual models-- 8. Imperfect experiments: bounds and counterfactuals-- 9. Probability of causation: interpretation and identification-- Epilogue: the art and science of cause and effect.
  • (source: Nielsen Book Data)9780521773621 20160528
Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence, business, epidemiology, social science and economics.
(source: Nielsen Book Data)9780521773621 20160528
Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections and statistical associations. Cited in more than 2,100 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers' questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interest to students and professionals in a wide variety of fields.
(source: Nielsen Book Data)9780521895606 20160528
Engineering Library (Terman), eReserve
MS&E-355-01
Book
xii, 548 p. : ill. ; 26 cm.
  • 1. Introduction-- 2. Propositional logic-- 3. Probability calculus-- 4. Bayesian networks-- 5. Building Bayesian networks-- 6. Inference by variable elimination-- 7. Inference by factor elimination-- 8. Inference by conditioning-- 9. Models for graph decomposition-- 10. Most likely instantiations-- 11. The complexity of probabilistic inference-- 12. Compiling Bayesian networks-- 13. Inference with local structure-- 14. Approximate inference by belief propagation-- 15. Approximate inference by stochastic sampling-- 16. Sensitivity analysis-- 17. Learning: the maximum likelihood approach-- 18. Learning: the Bayesian approach-- Appendix A: notation-- Appendix B: concepts from information theory-- Appendix C: fixed point iterative methods-- Appendix D: constrained optimization.
  • (source: Nielsen Book Data)9780521884389 20160528
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 treatment of exact algorithms covers the main inference paradigms based on elimination and conditioning and includes advanced methods for compiling Bayesian networks, time-space tradeoffs, and exploiting local structure of massively connected networks. The treatment of approximate algorithms covers the main inference paradigms based on sampling and optimization and includes influential algorithms such as importance sampling, MCMC, and belief propagation. 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.
(source: Nielsen Book Data)9780521884389 20160528
Engineering Library (Terman), eReserve
MS&E-355-01
Book
xvii, 318 p. : ill. ; 25 cm.
  • Introduction.- Networks.- Probabilities.- Probabilistic Networks.- Solving Probabilistic Networks.- Eliciting the Model.- Modeling Techniques.- Data-Driven Modeling.- Conflict Analysis.- Sensitivity Analysis.- Value of Information Analysis.
  • (source: Nielsen Book Data)9780387741000 20160528
Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification, troubleshooting, and data mining under uncertainty. "Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis" provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. 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/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.
(source: Nielsen Book Data)9780387741000 20160528
Engineering Library (Terman), eReserve
MS&E-355-01
Book
xvi, 447 p. : ill. ; 24 cm.
Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering. This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis. The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams. The reader is introduced to the two types of frameworks through examples and exercises, which also instruct the reader on how to build these models. The book is a new edition of "Bayesian Networks and Decision Graphs" by Finn V. Jensen. The new edition is structured into two parts. The first part focuses on probabilistic graphical models. Compared with the previous book, the new edition also includes a thorough description of recent extensions to the Bayesian network modeling language, advances in exact and approximate belief updating algorithms, and methods for learning both the structure and the parameters of a Bayesian network. The second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision processes and partially ordered decision problems. The authors also provide a well-founded practical introduction to Bayesian networks, object-oriented Bayesian networks, decision trees, influence diagrams (and variants hereof), and Markov decision processes. give practical advice on the construction of Bayesian networks, decision trees, and influence diagrams from domain knowledge. give several examples and exercises exploiting computer systems for dealing with Bayesian networks and decision graphs. This book presents a thorough introduction to state-of-the-art solution and analysis algorithms. The book is intended as a textbook, but it can also be used for self-study and as a reference book.
(source: Nielsen Book Data)9780387682815 20160528
Engineering Library (Terman)
MS&E-355-01
Book
xiv, 605 p. : ill. ; 24 cm.
  • Contents: Rule-based expert systems.- Probabilistic expert systems.- Some concepts of graphs.- Building probabalistic models.- Graphically specified models.- Extending graphically specified models.- Exact propagation in probabilistic network models.- Approximate propagation methods.- Symbolic propagation of evidence.- Learning Bayesian models.- Case studies.
  • (source: Nielsen Book Data)9780387948584 20160528
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.
(source: Nielsen Book Data)9780387948584 20160528
Engineering Library (Terman)
MS&E-355-01
Book
xxix, 465 p. : ill. ; 24 cm.
  • Partial table of contents: MODEL FORMULATION AND ANALYSIS. From Influence to Relevance to Knowledge (R. Howard). Complexity, Calibration and Causality in Influence Diagrams (T. Speed). THEORETICAL FOUNDATIONS. Statistical Principles on Graphs (J. Smith). Influence and Belief Adjustment (M. Goldstein). PROBLEMS AND APPLICATIONS: INDUSTRIAL. Real Time Influence Diagrams for Monitoring and Controlling Mechanical Systems (A. Agonino & K. Ramamurthi). A Socio-technical Approach to Assessing Human Reliability (L. Phillips, et al.). Bayesian Updating of Event Tree Parameters to Predict High Risk Incidents (R. Oliver & H. Yang). PROBLEMS AND APPLICATIONS: MEDICAL. EFFICIENCY AND COMPUTATIONAL ISSUES. Towards Efficient Probabilistic Diagnosis in Multiply Connected Belief Networks (M. Henrion). Towards Better Assessment and Sensitivity Procedures (R. Korsan). Summary Observations (R. Howard). Glossary. Index.
  • (source: Nielsen Book Data)9780471923817 20160528
These are the proceedings of a conference which reflected the growing interest in the use of influence diagrams, belief nets and graph-related models for prediction and decision analysis in engineering, statistics, operations research, management science, medicine and artificial intelligence. The conference brought together decision theory and operations research analysts, statisticians, computer scientists, engineers and experts from a variety of disciplines, to discuss the theory and applications of influence diagrams (IDs). These people were all from communities that use probabilistic models, mathematical models and structures for the propagation of evidence, statistical inference and prediction. The result is a sample of diverse research results and applications where IDs are being used. IDs were originally developed for use in the practice of decision analysis. When modelling practical problems it is necessary to combine different components of the problem into a coherent and mutually acceptable description. IDs are a powerful tool to facilitate communication between groups of clients and the decision analyst or modeller and to represent changing assumptions in a graphical manner that reveals difficult independence assumptions. They help to focus on internal dependencies as a whole rather than in disjointed sections. The conference concluded that IDs are valuable for solving difficult problems associated with probabilistic models, representation of large amounts of information, prediction, inference and decision-making.
(source: Nielsen Book Data)9780471923817 20160528
Engineering Library (Terman)
MS&E-355-01
Book
xiii, 433 p. : ill. ; 25 cm.
  • Background: probabilistic considerations-- graph theoretic considerations-- rule based expert systems: subjective Bayesian method-- the odds method-- causal networks: the use of causal networks in expert systems-- fusion, propagation and structuring in causal networks-- "marrying" and "filling in" in causal networks-- maximum entropy methods in causal networks-- abductive inference - the set covering method-- the probabilistic causal model-- the odds method applied to set covering-- higher order probabilities - the use of intervals instead of point values-- the use of causal networks to express uncertainty in point values.
  • (source: Nielsen Book Data)9780471618409 20160527
An introduction to the use of probabilistic techniques in the design of expert systems, the book provides a background in probability and graph theory. It shows how to use the probability calculus in modelling reasoning under uncertainty, discusses subjective Bayesian analysis and the odds method and covers the theoretical foundations of causal networks, including Pearl's method of belief propagation and fusion in causal networks. Each chapter is split into two sections - the first section presents the theory and the second explains the technique's use and offers examples.
(source: Nielsen Book Data)9780471618409 20160527
Engineering Library (Terman)
MS&E-355-01
Book
x, 768 p. : ill. ; 28 cm.
Most everyday reasoning and decision making is based on uncertain premises. This volume collects 42 key papers from the literature, addressing the methods that have been used in artificial intelligence to build systems with the ability to manage uncertainty. The editors have added volume and sectio.
(source: Nielsen Book Data)9781558601253 20160528
Engineering Library (Terman)
MS&E-355-01
Book
xix, 552 p. : ill. ; 24 cm.
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.
(source: Nielsen Book Data)9780934613736 20160605
Engineering Library (Terman)
MS&E-355-01
Book
1 online resource (1 volume) : illustrations.
eReserve
MS&E-355-01
Book
2 v. : ill. ; 28 cm.
  • v. 1. General collection.
  • v. 2. Professional collection.
Green Library, Engineering Library (Terman), SAL3 (off-campus storage), Science Library (Li and Ma)
MS&E-355-01