Advances in the understanding and use of conditional independence
Annals of Mathematics and Artificial Intelligence
Local computation with valuations from a commutative semigroup
Annals of Mathematics and Artificial Intelligence
An Alternative to Outward Propagation for Dempster-Shafer Belief Functions
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
About Conditional Belief Function Independence
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Approximate factorizations of distributions and the minimum relative entropy principle
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
The Estimation of Distributions and the Minimum Relative Entropy Principle
Evolutionary Computation
The Dempster--Shafer calculus for statisticians
International Journal of Approximate Reasoning
International Journal of Artificial Intelligence in Education
Extending stochastic ordering to belief functions on the real line
Information Sciences: an International Journal
Belief functions on real numbers
International Journal of Approximate Reasoning
Conflicts within and between belief functions
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Approximate learning in complex dynamic Bayesian networks
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A target classification decision aid
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Inference about constrained parameters using the elastic belief method
International Journal of Approximate Reasoning
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From the Publisher:This innovative volume explores graphical models using belieffunctions as a representation of uncertainty, offering an alternative approach to problems where probability proves inadequate. Graphical Belief Modeling makes it easy to compare the two approaches while evaluating their relative strengths and limitations. The author examines both theory and computation, incorporating practical notes from the author's own experience with the BELIEF software package. As one of the first volumes to apply the Dempster-Shafer belief functions to a practical model, a substantial portion of the book is devoted to a single example--calculating the reliability of a complex system. This special feature enables readers to gain a thorough understanding of the application of this methodology. The first section provides a description of graphical belief models and probablistic graphical models that form an important subset: the second section discusses the algorithm used in the manipulation of graphical models: the final segment of the book offers a complete description of the risk assessment example, as well as the methodology used to describe it. Graphical Belief Modeling offers researchers and graduate students in artificial intelligence and statistics more than just a new approach to an old reliability task: it provides them with an invaluable illustration of the process of graphical belief modeling.