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June 2003 Model selection for hidden Markov models of ion channel data by reversible jump Markov chain Monte Carlo
Mathisca C.M. De Gunst, Barry Schouten
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Bernoulli 9(3): 373-393 (June 2003). DOI: 10.3150/bj/1065444810


Ion channels are proteins that are located in the membranes of cells and are capable of conducting ions through the membrane. The ion channel is not always `open' for transport. The ion channel molecule may reside in several configurations, some of which correspond to an open channel and others to a closed channel. The transitions of the channel between the different configurational states have a random nature. Markov processes are often used to describe this randomness. In practice, there often exist a number of candidate Markov models. The objective of this paper is the selection of a Markov model from a finite collection of such models. We propose a Bayesian setting in which the model indicator itself is viewed as a random variable, and we develop a reversible jump Markov chain Monte Carlo (MCMC) algorithm in order to generate a sample from the posterior distribution of the model indicator given the data of a single-channel recording. A hidden Markov model is used to incorporate the correlated noise in recordings and the effects of filters that are present in the experimental set-up. The reversible jump MCMC sampler is applied to both simulated and recorded data sets.


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Mathisca C.M. De Gunst. Barry Schouten. "Model selection for hidden Markov models of ion channel data by reversible jump Markov chain Monte Carlo." Bernoulli 9 (3) 373 - 393, June 2003.


Published: June 2003
First available in Project Euclid: 6 October 2003

zbMATH: 1042.92011
MathSciNet: MR1997489
Digital Object Identifier: 10.3150/bj/1065444810

Keywords: Markov chain Monte Carlo , maximum a posteriori estimator , model identification , posterior distribution , single-channel recordings

Rights: Copyright © 2003 Bernoulli Society for Mathematical Statistics and Probability


Vol.9 • No. 3 • June 2003
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