Bernoulli

  • Bernoulli
  • Volume 9, Number 3 (2003), 373-393.

Model selection for hidden Markov models of ion channel data by reversible jump Markov chain Monte Carlo

Mathisca C.M. De Gunst and Barry Schouten

Full-text: Open access

Abstract

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.

Article information

Source
Bernoulli, Volume 9, Number 3 (2003), 373-393.

Dates
First available in Project Euclid: 6 October 2003

Permanent link to this document
https://projecteuclid.org/euclid.bj/1065444810

Digital Object Identifier
doi:10.3150/bj/1065444810

Mathematical Reviews number (MathSciNet)
MR1997489

Zentralblatt MATH identifier
1042.92011

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

Citation

De Gunst, Mathisca C.M.; Schouten, Barry. Model selection for hidden Markov models of ion channel data by reversible jump Markov chain Monte Carlo. Bernoulli 9 (2003), no. 3, 373--393. doi:10.3150/bj/1065444810. https://projecteuclid.org/euclid.bj/1065444810


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