Learning from imprecise data: possibilistic graphical models.

DOI10.1016/S0167-9473(01)00071-8zbMath1072.68627OpenAlexW2065977857MaRDI QIDQ5958476

Christian Borgelt, Rudolf Kruse

Publication date: 3 March 2002

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-9473(01)00071-8

zbMATH Keywords

Graphical modelsLearning from dataPossibilistic networks

Mathematics Subject Classification ID

Reasoning under uncertainty in the context of artificial intelligence (68T37) Circuits, networks (94C99) Artificial intelligence (68T99)

Related Items (4)

Qualitative possibilistic influence diagrams based on qualitative possibilistic utilities ⋮ Inference in possibilistic network classifiers under uncertain observations ⋮ Possibility theory and statistical reasoning ⋮ Jeffrey's rule of conditioning in a possibilistic framework

Uses Software

• PULCinella

Cites Work

• On the shortest spanning subtree of a graph and the traveling salesman problem
• Structure identification in relational data
• A Bayesian method for the induction of probabilistic networks from data
• The context model: An integrating view of vagueness and uncertainty
• Learning Bayesian networks: The combination of knowledge and statistical data
• An Introduction to Spatial Point Processes and Markov Random Fields
• Approximating discrete probability distributions with dependence trees
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