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• W3100647506 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." @default.
• W3100647506 created "2020-11-23" @default.
• W3100647506 creator A5033845924 @default.
• W3100647506 date "2007-01-23" @default.
• W3100647506 modified "2023-10-16" @default.
• W3100647506 title "Gibbs sampler by sampling-importance-resampling" @default.
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• W3100647506 doi "https://doi.org/10.1007/s00190-006-0121-1" @default.
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