Gibbs sampler by sampling-importance-resampling |
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| Authors: | K R Koch |
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| Institution: | (1) Institute for Theoretical Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, Germany |
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| Abstract: | Among the Markov chain Monte Carlo methods, the Gibbs sampler has the advantage that it samples from the conditional distributions
for each unknown parameter, thus decomposing the sample space. In the case the conditional distributions are not tractable,
the Gibbs sampler by means of sampling-importance-resampling is presented here. It uses the prior density function of a Bayesian
analysis as the importance sampling distribution. This leads to a fast convergence of the Gibbs sampler as demonstrated by
the smoothing with preserving the edges of 3D images of emission tomography. |
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| Keywords: | Markov chain Monte Carlo method Gibbs sampler Sampling-importance-resampling Digital image smoothing Positron emission tomography |
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