A Fast Method for sampling from Laplacian Type Distributions
نویسنده
چکیده
This paper deals with the problem of generating samples from a commonly used form of Laplacian distribution. The algorithm was developed particularly for use in generating samples from priors which deene models for images. It is shown that by ranking the independent variables in the distribution, an analytic expression for the Cumulative Density function can 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 nite 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.
منابع مشابه
A kernel-independent fast multipole algorithm
We present a new fast multipole method for particle simulations. The main feature of our algorithm is that is kernel independent, in the sense that no analytic expansions are used to represent the far field. Instead we use equivalent densities, which we compute by solving small Dirichlet-type boundary value problems. The translations from the sources to the induced potentials are accelerated by...
متن کاملTransmission of Cholera Disease with Laplacian and Triangular Parameters
A mathematical model has been introduced for the transmission dynamics of cholera disease by GQ Sun et al. recently. In this study, we add Laplacian and Triangular random effects to this model and analyze the variation of results for both cases. The expectations and coefficients of variation are compared for the random models and the results are used to comment on the differences and similarit...
متن کاملFast denoising of surface meshes with intrinsic texture
We describe a fast, dynamic, multiscale iterative method that is designed to smooth, but not over-smooth, noisy triangle meshes. Our method not only preserves sharp features but also retains visually meaningful fine scale components or details, referred to as intrinsic texture. An anisotropic Laplacian (AL) operator is first developed. It is then embedded in an iteration that gradually and adap...
متن کاملMulti-level Low-rank Approximation-based Spectral Clustering for image segmentation
Spectral clustering is a well-known graph-theoretic approach of finding natural groupings in a given dataset, and has been broadly used in image segmentation. Nowadays, High-Definition (HD) images are widely used in television broadcasting and movies. Segmenting these high resolution images presents a grand challenge to the current spectral clustering techniques. In this paper, we propose an ef...
متن کاملEstimating Ratios of Normalizing Constants Using Linked Importance Sampling
Abstract. Ratios of normalizing constants for two distributions are needed in both Bayesian statistics, where they are used to compare models, and in statistical physics, where they correspond to differences in free energy. Two approaches have long been used to estimate ratios of normalizing constants. The ‘simple importance sampling’ (SIS) or ‘free energy perturbation’ method uses a sample dra...
متن کامل