A Solution for the Learning Problem in Evidential (Partially) Hidden Markov Models Based on Conditional Belief Functions and EM

DOI10.1007/978-3-319-40596-4_26zbMath1452.68156OpenAlexW2490079206MaRDI QIDQ5115737

Emmanuel Ramasso

Publication date: 18 August 2020

Published in: Information Processing and Management of Uncertainty in Knowledge-Based Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-40596-4_26

zbMATH Keywords

Markov chainbelief functionsparameter learningevidential latent variableevidential temporal graphical model

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Reasoning under uncertainty in the context of artificial intelligence (68T37) Probabilistic graphical models (62H22)

Uses Software

• PRMLT

Cites Work

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• Multisensor triplet Markov chains and theory of evidence
• Learning from partially supervised data using mixture models and belief functions
• Belief functions: The disjunctive rule of combination and the generalized Bayesian theorem
• Forward-Backward-Viterbi Procedures in the Transferable Belief Model for State Sequence Analysis Using Belief Functions
• Symbolic and Quantitative Approaches to Reasoning with Uncertainty
• A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains