The application of the matrix calculus to belief functions.
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Publication:1399494
DOI10.1016/S0888-613X(02)00066-XzbMath1033.68119MaRDI QIDQ1399494
Publication date: 30 July 2003
Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)
Related Items (20)
Proposition and learning of some belief function contextual correction mechanisms ⋮ Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence ⋮ Idempotent conjunctive and disjunctive combination of belief functions by distance minimization ⋮ An improved quantum combination method of mass functions based on supervised learning ⋮ Matrix operations in random permutation set ⋮ Generalised Max Entropy Classifiers ⋮ Distances in evidence theory: comprehensive survey and generalizations ⋮ Belief functions contextual discounting and canonical decompositions ⋮ New distances between bodies of evidence based on Dempsterian specialization matrices and their consistency with the conjunctive combination rule ⋮ Preference Elicitation with Uncertainty: Extending Regret Based Methods with Belief Functions ⋮ Combination of partially non-distinct beliefs: the cautious-adaptive rule ⋮ From set relations to belief function relations ⋮ Complementary Lipschitz continuity results for the distribution of intersections or unions of independent random sets in finite discrete spaces ⋮ A new rule to combine dependent bodies of evidence ⋮ Effectiveness assessment of cyber-physical systems ⋮ In memoriam: Philippe Smets (1938--2005) ⋮ An Evidential Measure of Risk in Evidential Markov Chains ⋮ Canonical decomposition of belief functions based on Teugels' representation of the multivariate Bernoulli distribution ⋮ In memoriam: Philippe Smets (1938--2005) ⋮ Interpreting evidential distances by connecting them to partial orders: application to belief function approximation
Cites Work
- The only crooked power functions are \(x^{2^k+2^l}\)
- The transferable belief model
- The entailment principle for dempster—shafer granules
- Computational aspects of the Mobius transformation of graphs
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