PROBE (Progressive Removal of Blur Residual) is a recursive framework for blind deblurring. Using the elementary modified inverse filter at its core, PROBE’s experimental performance meets or exceeds the state of the art, both visually and quantitatively. Remarkably, PROBE lends itself to analysis that reveals its convergence properties. PROBE is motivated by recent ideas on progressive blind d...

The retrieval of wind vectors from satellite scatterometers is a non-linear inverse problem. A common approach to solving inverse problems is to adopt a Bayesian framework and infer the posterior distribution of the parameters of interest given the observations using a likelihood model relating the observations to the parameters, and a prior distribution over the parameters. In this paper we sh...

The retrieval of wind vectors from satellite scatterometer observations is a non-linear inverse problem. A common approach to solving inverse problems is to adopt a Bayesian framework and to infer the posterior distribution of the parameters of interest given the observations by using a likelihood model relating the observations to the parameters, and a prior distribution over the parameters. W...

In this article we obtain the first passage time distribution of α-stable Levy processes. We derive moment estimators parameters α-inverse Gaussian laws and also their asymptotic distribution.

In this paper we propose a novel framework for the construction of sparsity-inducing priors. In particular, we define such priors as a mixture of exponential power distributions with a generalized inverse Gaussian density (EP-GIG). EP-GIG is a variant of generalized hyperbolic distributions, and the special cases include Gaussian scale mixtures and Laplace scale mixtures. Furthermore, Laplace s...

 Spatial generalized linear mixed models are used commonly for modelling non-Gaussian discrete spatial responses. We present an algorithm for parameter estimation of the models using Laplace approximation of likelihood function. In these models, the spatial correlation structure of data is carried out by random effects or latent variables. In most spatial analysis, it is assumed that rando...

This paper documents the application of the Poisson Inverse Gaussian (PIG) regression model for modeling motor vehicle crash data. The PIG distribution, which mixes the Poisson distribution and Inverse Gaussian distribution, has the potential for modeling highly dispersed count data due to the flexibility of Inverse Gaussian distribution. The objectives of this paper were to evaluate the applic...