Classic Works of the Dempster-Shafer Theory of Belief Functions

Classic Works of the Dempster-Shafer Theory of Belief Functions: An Introduction. - New Methods for Reasoning Towards Posterior Distributions Based on Sample Data. - Upper and Lower Probabilities Induced by a Multivalued Mapping. - A Generalization of Bayesian Inference. - On Random Sets and Belief Functions. - Non-Additive Probabilities in the Work of Bernoulli and Lambert. - Allocations of Probability. - Computational Methods for A Mathematical Theory of Evidence. - Constructive Probability. - Belief Functions and Parametric Models. - Entropy and Specificity in a Mathematical Theory of Evidence. - A Method for Managing Evidential Reasoning in a Hierarchical Hypothesis Space. - Languages and Designs for Probability Judgment. - A Set-Theoretic View of Belief Functions. - Weights of Evidence and Internal Conflict for Support Functions. - A Framework for Evidential-Reasoning Systems. - Epistemic Logics, Probability, and the Calculus of Evidence. - Implementing Dempster s Rule for Hierarchical Evidence. - Some Characterizations of Lower Probabilities and Other Monotone Capacities through the use of Möbius Inversion. - Axioms for Probability and Belief-Function Propagation. - Generalizing the Dempster Shafer Theory to Fuzzy Sets. - Bayesian Updating and Belief Functions. - Belief-Function Formulas for Audit Risk. - Decision Making Under Dempster Shafer Uncertainties. - Belief Functions: The Disjunctive Rule of Combination and the Generalized Bayesian Theorem. - Representation of Evidence by Hints. - Combining the Results of Several Neural Network Classifiers. - The Transferable Belief Model. - A k-Nearest Neighbor Classification Rule Based on Dempster-Shafer Theory. - Logicist Statistics II: Inference.