Exact and Approximate Inference for Annotating Graphs with Structural SVMs
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
MRF inference by k-fan decomposition and tight Lagrangian relaxation
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Statement networks development environment REx
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part II
Probabilistic management of OCR data using an RDBMS
Proceedings of the VLDB Endowment
Position Paper: The role of expert opinion in environmental modelling
Environmental Modelling & Software
Efficiently adapting graphical models for selectivity estimation
The VLDB Journal — The International Journal on Very Large Data Bases
Multilevel Bayesian networks for the analysis of hierarchical health care data
Artificial Intelligence in Medicine
An Inference Engine for Estimating Outside States of Clinical Test Items
ACM Transactions on Management Information Systems (TMIS)
Refining a Bayesian Network using a Chain Event Graph
International Journal of Approximate Reasoning
Understanding users' behavior with software operation data mining
Computers in Human Behavior
Sensitivity to hyperprior parameters in Gaussian Bayesian networks
Journal of Multivariate Analysis
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WINNER OF THE 2001 DEGROOT PRIZE! Probabilistic expert systems are graphical networks that support the modelling of uncertainty and decisions in large complex domains, while retaining ease of calculation. Building on original research by the authors over a number of years, this book gives a thorough and rigorous mathematical treatment of the underlying ideas, structures, and algorithms, emphasizing those cases in which exact answers are obtainable. It covers both the updating of probabilistic uncertainty in the light of new evidence, and statistical inference, about unknown probabilities or unknown model structure, in the light of new data. The careful attention to detail will make this work an important reference source for all those involved in the theory and applications of probabilistic expert systems. This book was awarded the first DeGroot Prize by the International Society for Bayesian Analysis for a book making an important, timely, thorough, and notably original contribution to the statistics literature.