A solution for the learning problem in Evidential (Partially) Hidden Markov Models based on Conditional Belief Functions and EM
Résumé
Evidential Hidden Markov Models (EvHMM) is a particular
Evidential Temporal Graphical Model that aims at statistically repre-
senting the kynetics of a system by means of an Evidential Markov Chain
and an observation model. Observation models are made of mixture of
densities to represent the inherent variability of sensor measurements,
whereas uncertainty on the latent structure, that is generally only par-
tially known due to lack of knowledge, is managed by Dempster-Shafer's
theory of belief functions. This paper is dedicated to the presentation of
an Expectation-Maximization procedure to learn parameters in EvHMM.
Results demonstrate the high potential of this method illustrated on
complex datasets originating from turbofan engines where the aim is to
provide early warnings of disfunction.
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