Learning Structure of Bayesian Networks by Using Possibilistic Upper Entropy

DOI10.1007/978-3-319-10765-3_11zbMath1336.68222OpenAlexW16684999MaRDI QIDQ2808104

Mathieu Serrurier, Henri Prade

Publication date: 26 May 2016

Published in: Strengthening Links Between Data Analysis and Soft Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-10765-3_11

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Cites Work

• Possibility theory and statistical reasoning
• When upper probabilities are possibility measures
• Fuzzy sets as a basis for a theory of possibility
• Probability-possibility transformations, triangular fuzzy sets, and probabilistic inequalities
• An informational distance for estimating the faithfulness of a possibility distribution, viewed as a family of probability distributions, with respect to data
• Upper entropy of credal sets. Applications to credal classification

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