Fast method for sampling from Laplacian-type distributions

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.