Explainable AI using MAP-independence

DOI10.1007/978-3-030-86772-0_18OpenAlexW3199606576MaRDI QIDQ2146024

Johan Kwisthout

Publication date: 15 June 2022

Full work available at URL: https://doi.org/10.1007/978-3-030-86772-0_18

zbMATH Keywords

relevancecomputational complexityBayesian networksexplainable AImost probable explanations

Mathematics Subject Classification ID

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

Related Items (2)

Motivating explanations in Bayesian networks using MAP-independence ⋮ Efficient search for relevance explanations using MAP-independence in Bayesian networks

Cites Work

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• Most probable explanations in Bayesian networks: complexity and tractability
• The complexity of combinatorial problems with succinct input representation
• The computational complexity of probabilistic inference using Bayesian belief networks
• Tree-Width and the Computational Complexity of MAP Approximations in Bayesian Networks
• On Stopping Evidence Gathering for Diagnostic Bayesian Networks
• Relevance of Evidence in Bayesian Networks
• Modeling and Reasoning with Bayesian Networks
• Most Inforbable Explanations: Finding Explanations in Bayesian Networks That Are Both Probable and Informative

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