Interval probability propagation

DOI10.1016/0888-613X(92)90006-LzbMath0769.68113MaRDI QIDQ1207963

Bjørnar Tessem

Publication date: 16 May 1993

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

zbMATH Keywords

uncertaintyintelligent systemsBelief networksbelief updatingconvex polytope representationnetwork propagationpropagation of singleton interval probabilities

Mathematics Subject Classification ID

Knowledge representation (68T30) Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence (68T35)

Related Items (21)

Using probability trees to compute marginals with imprecise probabilities ⋮ Approximate algorithms for credal networks with binary variables ⋮ Graphical models for imprecise probabilities ⋮ Inference in credal networks: Branch-and-bound methods and the A/R+ algorithm ⋮ Measures of uncertainty in expert systems ⋮ Hill-climbing and branch-and-bound algorithms for exact and approximate inference in credal networks ⋮ Thirty years of credal networks: specification, algorithms and complexity ⋮ A general evidential reasoning algorithm for multi-attribute decision analysis under interval uncertainty ⋮ Interval probability propagation ⋮ Pari-mutuel probabilities as an uncertainty model ⋮ Bayesian network implementation of Levi's epistemic utility decision theory ⋮ Incremental conditioning of lower and upper probabilities ⋮ Increased robustness of Bayesian networks through probability intervals ⋮ Computing probability intervals with simulated annealing and probability trees ⋮ Unnamed Item ⋮ 2U: an exact interval propagation algorithm for polytrees with binary variables ⋮ Credal networks ⋮ Interval-Valued Degrees of Belief: Applications of Interval Computations to Expert Systems and Intelligent Control ⋮ Computing posterior upper expectations ⋮ Quasi-Bayesian analysis using imprecise probability assessments and the generalized Bayes' rule ⋮ The naive credal classifier

Cites Work

• Fusion, propagation, and structuring in belief networks
• Bayesian and non-Bayesian evidential updating
• Interval probability propagation
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