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A New Method for Inferring Hidden Markov Models from Noisy Time Sequences

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posted on 2013-06-18, 14:27 authored by David Kelly, Mark Dillingham, Andrew Hudson, Karoline Wiesner
We present a new method for inferring hidden Markov models from noisy time sequences without the necessity of assuming a model architecture, thus allowing for the detection of degenerate states. This is based on the statistical prediction techniques developed by Crutchfield et al. and generates so called causal state models, equivalent in structure to hidden Markov models. The new method is applicable to any continuous data which clusters around discrete values and exhibits multiple transitions between these values such as tethered particle motion data or Fluorescence Resonance Energy Transfer (FRET) spectra. The algorithms developed have been shown to perform well on simulated data, demonstrating the ability to recover the model used to generate the data under high noise, sparse data conditions and the ability to infer the existence of degenerate states. They have also been applied to new experimental FRET data of Holliday Junction dynamics, extracting the expected two state model and providing values for the transition rates in good agreement with previous results and with results obtained using existing maximum likelihood based methods. The method differs markedly from previous Markov-model reconstructions in being able to uncover truly hidden states.

History

Citation

PLoS One, 2012, 7 (1), e29703 (9).

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Chemistry

Version

  • VoR (Version of Record)

Published in

PLoS One

Publisher

Public Library of Science

issn

1932-6203

eissn

1932-6203

Copyright date

2012

Available date

2013-06-18

Publisher version

http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029703

Language

en

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