Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Gibbs sampler by sampling-importance-resampling
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.
Gibbs sampler by sampling-importance-resampling
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.
Gibbs sampler by sampling-importance-resampling
Koch, K. R. (Autor:in)
Journal of Geodesy ; 81
2007
Aufsatz (Zeitschrift)
Englisch
BKL:
38.73
Geodäsie
Gibbs sampler by sampling-importance-resampling
| Online Contents | 2007
Gibbs sampler for computing and propagating large covariance matrices
| Online Contents | 2003
Time Series Analysis of BOD Data using the Gibbs Sampler
| Online Contents | 1996
Bayesian linear structural model updating using Gibbs sampler with modal data
| British Library Conference Proceedings | 2005