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Proceedings Paper

Fast method for sampling from Laplacian-type distributions
Author(s): Anil Christopher Kokaram; Miao-Dan Wu
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Paper Abstract

This paper deals with the problem of generating samples for a commonly used form of Laplacian distribution. The algorithm was developed particularly for use in generating samples from priors which define morsel for images. It is shown that by ranking the independent variables in the distribution, an analytic expression for the Cumulative Density function ca be derived. This can be used to generate random samples by transforming a uniformly distributed random variable. Issues of scaling are addressed which make the numerical application of these functions possible on finite precision machines. Some discussion is given about the convergence of the Gibbs sampler using this sampling method compared with using direct methods or the Metropolis algorithm.

Paper Details

Date Published: 22 September 1998
PDF: 8 pages
Proc. SPIE 3459, Bayesian Inference for Inverse Problems, (22 September 1998); doi: 10.1117/12.323810
Show Author Affiliations
Anil Christopher Kokaram, Trinity College, Dublin (Ireland)
Miao-Dan Wu, Univ. of Cambridge (United Kingdom)


Published in SPIE Proceedings Vol. 3459:
Bayesian Inference for Inverse Problems
Ali Mohammad-Djafari, Editor(s)

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