Fast method for sampling from Laplacian-type distributions

Anil Christopher Kokaram; Miao-Dan Wu

doi:10.1117/12.323810

22 September 1998 Fast method for sampling from Laplacian-type distributions

Anil Christopher Kokaram, Miao-Dan Wu

Proceedings Volume 3459, Bayesian Inference for Inverse Problems; (1998) https://doi.org/10.1117/12.323810
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1998, San Diego, CA, United States

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.

Citation Download Citation

Anil Christopher Kokaram and Miao-Dan Wu "Fast method for sampling from Laplacian-type distributions", Proc. SPIE 3459, Bayesian Inference for Inverse Problems, (22 September 1998); https://doi.org/10.1117/12.323810

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