title: A new method for inferring hidden markov models from noisy time sequences.
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.
authors.affiliation: School of Mathematics, University of Bristol, Bristol, United Kingdom. dk3531@bristol.ac.uk
authors.forename: David
authors.initials: D
authors.lastname: Kelly
authors.forename: Mark
authors.initials: M
authors.lastname: Dillingham
authors.forename: Andrew
authors.initials: A
authors.lastname: Hudson
authors.forename: Karoline
authors.initials: K
authors.lastname: Wiesner
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D045566:DNA, Cruciform / Q000032:analysis* / Q000235:genetics; D031541:Fluorescence Resonance Energy Transfer; D007700:Kinetics; D008390:Markov Chains; D008956:Models, Chemical; D015233:Models, Statistical
chemical_list: D045566:DNA, Cruciform