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A Bayesian restoration of an ion channel signal

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  • M. E. A. Hodgson

Abstract

We present a Bayesian method of ion channel analysis and apply it to a simulated data set. An alternating renewal process prior is assigned to the signal, and an autoregressive process is fitted to the noise. After choosing model hyperconstants to yield ‘uninformative’ priors on the parameters, the joint posterior distribution is computed by using the reversible jump Markov chain Monte Carlo method. A novel form of simulated tempering is used to improve the mixing of the original sampler.

Suggested Citation

  • M. E. A. Hodgson, 1999. "A Bayesian restoration of an ion channel signal," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 95-114.
    • Handle: RePEc:bla:jorssb:v:61:y:1999:i:1:p:95-114
    DOI: 10.1111/1467-9868.00165
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    Cited by:

    1. Chantal Guihenneuc & Judith Rousseau, 2002. "Laplace Expansions in MCMC Algorithms for Latent Variable Models," Working Papers 2002-13, Center for Research in Economics and Statistics.

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