Fusion and propagation of graphical belief models: an implementation and an example
Fusion and propagation of graphical belief models: an implementation and an example
Artificial Intelligence
Graphical Belief Modeling
Theory of Relational Databases
Theory of Relational Databases
Extending stochastic ordering to belief functions on the real line
Information Sciences: an International Journal
A betting interpretation for probabilities and Dempster--Shafer degrees of belief
International Journal of Approximate Reasoning
A decision theory for partially consonant belief functions
International Journal of Approximate Reasoning
A novel case based reasoning approach to radiotherapy planning
Expert Systems with Applications: An International Journal
Particle filtering in the Dempster--Shafer theory
International Journal of Approximate Reasoning
Constructing and evaluating alternative frames of discernment
International Journal of Approximate Reasoning
The conjunctive combination of interval-valued belief structures from dependent sources
International Journal of Approximate Reasoning
Inference about constrained parameters using the elastic belief method
International Journal of Approximate Reasoning
On Z-valuations using Zadeh's Z-numbers
International Journal of Intelligent Systems
Mathematical foundations for a theory of confidence structures
International Journal of Approximate Reasoning
Organizational Patterns for Security and Dependability: From Design to Application
International Journal of Secure Software Engineering
Belief function and multivalued mapping robustness in statistical estimation
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Computational issues of generalized fiducial inference
Computational Statistics & Data Analysis
Probabilistic inference for multiple testing
International Journal of Approximate Reasoning
Joint cumulative distribution functions for Dempster---Shafer belief structures using copulas
Fuzzy Optimization and Decision Making
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The Dempster-Shafer (DS) theory of probabilistic reasoning is presented in terms of a semantics whereby every meaningful formal assertion is associated with a triple (p,q,r) where p is the probability ''for'' the assertion, q is the probability ''against'' the assertion, and r is the probability of ''don't know''. Arguments are presented for the necessity of ''don't know''. Elements of the calculus are sketched, including the extension of a DS model from a margin to a full state space, and DS combination of independent DS uncertainty assessments on the full space. The methodology is applied to inference and prediction from Poisson counts, including an introduction to the use of join-tree model structure to simplify and shorten computation. The relation of DS theory to statistical significance testing is elaborated, introducing along the way the new concept of ''dull'' null hypothesis.