Approximate inference in hidden Markov models using iterative active state selection

@article{Vithanage2006ApproximateII,
  title={Approximate inference in hidden Markov models using iterative active state selection},
  author={Cheran M. Vithanage and Christophe Andrieu and Robert J. Piechocki},
  journal={IEEE Signal Processing Letters},
  year={2006},
  volume={13},
  pages={65-68}
}
The inferential task of computing the marginal posterior probability mass functions of state variables and pairs of consecutive state variables of a hidden Markov model is considered. This can be exactly and efficiently performed using a message passing scheme such as the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm. We present a novel iterative reduced complexity variation of the BCJR algorithm that uses reduced support approximations for the forward and backward messages, as in the M-BCJR… CONTINUE READING

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