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A Bayesian restoration of an ion channel signal

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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.

Keywords: Ion channels; Reversible jump Markov chain Monte Carlo computation; Signal restoration; Simulated tempering; Step functions

Document Type: Original Article


Affiliations: Macaulay Land Use Research Institute, Aberdeen, UK

Publication date: January 1, 1999

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