A New Method for inferring hidden Markov models from noisy time sequences

David E Kelly, Mark Dillingham, Andrew J Hudson, Karoline Wiesner

Research output: Contribution to journalArticle (Academic Journal)peer-review

25 Citations (Scopus)

Abstract

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.
Translated title of the contributionA New Method for inferring hidden Markov models from noisy time sequences
Original languageEnglish
Article numbere29703
Number of pages19
JournalPLoS ONE
Volume7
Issue number1
DOIs
Publication statusPublished - Jan 2012

Bibliographical note

Other: Published on-line 11 January 2012

Structured keywords

  • Jean Golding

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