Decision-theoretic specification of credal networks: a unified language for uncertain modeling with sets of Bayesian networks

DOI10.1016/j.ijar.2008.02.005zbMath1191.68670OpenAlexW2042744306MaRDI QIDQ962637

Marco Zaffalon, Alessandro Antonucci

Publication date: 7 April 2010

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

Full work available at URL: https://doi.org/10.1016/j.ijar.2008.02.005

zbMATH Keywords

Bayesian networksimprecise probabilitiesprobabilistic graphical modelscredal networkscredal setsconservative inference ruleconservative updating

Mathematics Subject Classification ID

Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items (9)

Coherence graphs ⋮ Updating credal networks is approximable in polynomial time ⋮ Cautious classification with data missing not at random using generative random forests ⋮ Thirty years of credal networks: specification, algorithms and complexity ⋮ Robust classification of multivariate time series by imprecise hidden Markov models ⋮ On the complexity of solving polytree-shaped limited memory influence diagrams with binary variables ⋮ The multilabel naive credal classifier ⋮ Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks ⋮ Approximate credal network updating by linear programming with applications to decision making

Cites Work

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• Updating beliefs with incomplete observations
• Fast algorithms for robust classification with Bayesian nets
• 2U: an exact interval propagation algorithm for polytrees with binary variables
• A computational theory of decision networks
• Using probability trees to compute marginals with imprecise probabilities
• Credal networks
• Strong conditional independence for credal sets
• Graphical models for imprecise probabilities
• Fundamental concepts of qualitative probabilistic networks

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