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UAI '02: Proceedings of the 18th Conference in Uncertainty in Artificial Intelligence (Paperback)

by Jack Breese (Editor), Daphne Koller (Editor) "Ancestral graphs provide a class of graphs that can encode conditional independence relations that arise in directed acyclic graph (DAG) models with latent and selection..." (more)
Key Phrases: credal sets, joint density trees, weak error stability, Morgan Kaufmann, New York, San Francisco (more...)

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Editorial Reviews

Product Description

Proceedings of the annual Conference on Uncertainty in Artificial Intelligence, available for 1991-present.

Since 1985, the Conference on Uncertainty in Artificial Intelligence (UAI) has been the primary international forum for exchanging results on the use of principled uncertain-reasoning methods in intelligent systems. The UAI Proceedings have become a basic reference for researches and practitioners who want to know about both theoretical advances and the latest applied developments in the field.

From the Back Cover
The UAI Proceedings have become a basic reference for researches and practitioners who want to know about both theoretical advances and the latest applied developments in the field.

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Product Details

Paperback: 600 pages
Publisher: Morgan Kaufmann (September 20, 2002)
Language: English
ISBN-10: 1558608974
ISBN-13: 978-1558608979
Product Dimensions: 10.9 x 8.4 x 1.2 inches
Shipping Weight: 3.1 pounds
Average Customer Review: No customer reviews yet. Be the first.
Amazon.com Sales Rank: #5,740,023 in Books (See Bestsellers in Books)

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Inside This Book (learn more)

First Sentence:
Ancestral graphs provide a class of graphs that can encode conditional independence relations that arise in directed acyclic graph (DAG) models with latent and selection variables, corresponding to marginalization and conditioning. Read the first page

Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
credal sets, joint density trees, weak error stability, factored particles, reward formulae, ancestral graphs, total clique size, apparent best response, credal networks, gamble inequalities, maxent models, presence forecasting, weak hypothesis stability, initial mixture weight, switching linear dynamical systems, conditional likelihood maximization, extended likelihood function, specified strong extensions, summarization game, consistent scoring criterion, relative worst case scenario, robust spanning tree, inducing path, naive conditioning, empirical mutual information

Key Phrases - Capitalized Phrases (CAPs): (learn more)
Morgan Kaufmann, New York, San Francisco, Stanford University, International Conference, Neural Information Processing Systems, Della Pietra, Department of Computer Science, Carnegie Mellon University, John Wiley, Microsoft Research, Annals of Statistics, Computer Science Department, San Mateo, Neural Computation, Monty Hall, Department of Statistics, Van Roy, Daphne Koller, Cambridge University Press, Fifteenth Conference, Journal of the Royal Statistical Society, Springer Verlag, Academic Press, High Freq

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Citations (learn more)

This book cites 28 books:

Elements of Information Theory by Thomas M. Cover on page 144, page 196, and page 584
Information Processing Systems: Local Area Networks : Carrier Sense Multiple Access With Collision Detection (Csma/CD Access Method and Physical L) by IEEE Computer Society on page 343, and page 584
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference by Judea Pearl on page 35, and page 551
Markov Decision Processes: Discrete Stochastic Dynamic Programming by Martin L. Puterman on page 568, and page 576
Asymptotic Approximations of Integrals (Computer Science and Scientific Computing) by R. Wong on page 443

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Books on Related Topics (learn more)

Learning in Graphical Models by Michael I. Jordan

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Handbook of Data Mining and Knowledge Discovery by Willi Klosgen

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