### Abstract

Original language | English |
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Pages (from-to) | 373-393 |

Journal | Bernoulli: A Journal of Mathematical Statistics and Probability |

Volume | 9 |

Issue number | 3 |

DOIs | |

Publication status | Published - 2003 |

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**Model selection for hidden Markov models of ion channel data by reversible jump Markov chain Monte Carlo.** / de Gunst, M.C.M.; Schouten, B.

Research output: Contribution to Journal › Article › Academic › peer-review

TY - JOUR

T1 - Model selection for hidden Markov models of ion channel data by reversible jump Markov chain Monte Carlo

AU - de Gunst, M.C.M.

AU - Schouten, B.

PY - 2003

Y1 - 2003

N2 - Ion channels are proteins that are located in the membranes of cells and are capable of conducting ions through the membrane. An 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. © 2003 ISI/BS.

AB - Ion channels are proteins that are located in the membranes of cells and are capable of conducting ions through the membrane. An 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. © 2003 ISI/BS.

U2 - 10.3150/bj/1065444810

DO - 10.3150/bj/1065444810

M3 - Article

VL - 9

SP - 373

EP - 393

JO - Bernoulli: A Journal of Mathematical Statistics and Probability

JF - Bernoulli: A Journal of Mathematical Statistics and Probability

SN - 1350-7265

IS - 3

ER -