Statistical Inference in Hidden Markov Models Using k-Segment Constraints

@inproceedings{Titsias2016StatisticalII,
  title={Statistical Inference in Hidden Markov Models Using k-Segment Constraints},
  author={Michalis K. Titsias and Christopher Yau and Christopher C. Holmes},
  booktitle={Journal of the American Statistical Association},
  year={2016}
}
Hidden Markov models (HMMs) are one of the most widely used statistical methods for analyzing sequence data. However, the reporting of output from HMMs has largely been restricted to the presentation of the most-probable (MAP) hidden state sequence, found via the Viterbi algorithm, or the sequence of most probable marginals using the forward-backward algorithm. In this article, we expand the amount of information we could obtain from the posterior distribution of an HMM by introducing linear… CONTINUE READING
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