Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/27991
Title: A New Method for Inferring Hidden Markov Models from Noisy Time Sequences
Authors: Kelly, David
Dillingham, Mark
Hudson, Andrew
Wiesner, Karoline
First Published: 11-Jan-2012
Publisher: Public Library of Science
Citation: PLoS One, 2012, 7 (1), e29703 (9).
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.
DOI Link: 10.1371/journal.pone.0029703
ISSN: 1932-6203
eISSN: 1932-6203
Links: http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0029703
http://hdl.handle.net/2381/27991
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright: © 2012 Kelly et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Appears in Collections:Published Articles, Dept. of Chemistry

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