Corpus ID: 59604499

Exploiting locality in high-dimensional factorial hidden Markov models

@article{Rimella2019ExploitingLI,
  title={Exploiting locality in high-dimensional factorial hidden Markov models},
  author={Lorenzo Rimella and Nick Whiteley},
  journal={ArXiv},
  year={2019},
  volume={abs/1902.01639}
}
  • Lorenzo Rimella, Nick Whiteley
  • Published 2019
  • Computer Science, Mathematics
  • ArXiv
  • We propose algorithms for approximate filtering and smoothing in high-dimensional factorial hidden Markov models. The approximation involves discarding, in a principled way, likelihood factors according a notion of locality in a factor graph associated with the emission distribution. This allows the exponential-in-dimension cost of exact filtering and smoothing to be avoided. We prove that the approximation accuracy, measured in a local total variation norm, is `dimension-free' in the sense… CONTINUE READING
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    Dynamic Bayesian Neural Networks
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