Making Use of Partial Knowledge About Hidden States in HMMs: An Approach Based on Belief Functions

@article{Ramasso2014MakingUO,
  title={Making Use of Partial Knowledge About Hidden States in HMMs: An Approach Based on Belief Functions},
  author={Emmanuel Ramasso and Thierry Denoeux},
  journal={IEEE Transactions on Fuzzy Systems},
  year={2014},
  volume={22},
  pages={395-405}
}
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 learned using the evidential expectation-maximization algorithm, a recently introduced variant of the expectation-maximization algorithm for maximum likelihood estimation based on uncertain data… CONTINUE READING
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