Making use of partial knowledge about hidden states in HMMs : an approach based on belief functions.

Emmanuel Ramasso (1) , Thierry Denoeux (2)

1 FEMTO-ST - Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174)
2 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]

This paper addresses the problem of parameter estimation and state prediction in Hidden Markov Models (HMMs) based on observed outputs and partial knowledge of hidden states expressed in the belief function framework. The usual HMM model is recovered when the belief functions are vacuous. Parameters are learnt using the Evidential Expectation- Maximization algorithm, a recently introduced variant of the Expectation-Maximization algorithm for maximum likelihood estimation based on uncertain data. The inference problem, i.e., finding the most probable sequence of states based on observed outputs and partial knowledge of states, is also addressed. Experimental results demonstrate that partial information about hidden states, when available, may substantially improve the estimation and prediction performances.

Domaines: Automatique / Robotique

Emmanuel Ramasso, Thierry Denoeux. Making use of partial knowledge about hidden states in HMMs : an approach based on belief functions.. IEEE Transactions on Fuzzy Systems, 2014, 22 (2), pp.395-405. ⟨10.1109/TFUZZ.2013.2259496⟩. ⟨hal-00834177⟩