The resolution of many large-scale inverse problems using MCMC methods requires a step of drawing samples from a high dimensional Gaussian distribution. While direct Gaussian sampling techniques, such as those based on Cholesky factorization, induce an excessive numerical complexity and memory requirement, sequential coordinate sampling methods present a low rate of convergence. Based on the re...

We investigate the relationship between the structure of a discrete graphical model and the support of the inverse of a generalized covariance matrix. We show that for certain graph structures, the support of the inverse covariance matrix of indicator variables on the vertices of a graph reflects the conditional independence structure of the graph. Our work extends results that have previously ...

We consider the limiting statistical properties of fluctuations of statistical mechanics models. The two random interfaces of one-dimensional statistical physics models is modeled and investigated in the present paper. The two random interfaces are constructed by assuming that there is a specified value of the large area in the intermediate region of the two random interfaces, and the two rando...

We propose an information complexity-based regularization parameter selection method for solution of ill-conditioned inverse problems. The regularization parameter is selected to be the minimizer of the Kullback-Leibler (KL) distance between the unknown data-generating distribution and the fitted distribution. The KL distance is approximated by an information complexity (ICOMP) criterion develo...

Two major performance degrading factors in free space optical communication systems are rainfall and atmospheric turbulence. We study the outage probability and bit-error rate for free-space communication links with spatial diversity and Gaussian-Schell electromagnetism beams over the raining turbulence fading channels by double inverse Gaussian distribution proposed in this paper. Assuming int...

This paper considers conditional Gaussian networks. The parameters in the network are learned by using conjugate Bayesian analysis. As conjugate local priors, we apply the Dirichlet distribution for discrete variables and the Gaussian-inverse gamma distribution for continuous variables, given a configuration of the discrete parents. We assume parameter independence and complete data. Further, t...

We detail a Bayesian interpolation procedure for linearin-the-parameter models which combines both effective complexity control and robustness to outliers. Robustness is obtained by adopting a Student-t noise distribution, defined hierarchically in terms of an inverse-Gamma prior distribution over individual Gaussian observation variances. Importantly, this hierarchical definition enables pract...

The Poisson model is frequently employed to describe count data, but in a Bayesian context it leads to an analytically intractable posterior probability distribution. In this work, we analyze a variational Gaussian approximation to the posterior distribution arising from the Poisson model with a Gaussian prior. This is achieved by seeking an optimal Gaussian distribution minimizing the Kullback...

in this paper, the detector of moving target in mimo radar with widely separated antenna in the presence of α-stable clutter is proposed. due to the complexity of unknown parameter estimation in the presence of target, the rao test is applied. the α-stable model is a general model that can applied to different environments with appropriate selection of parameters. the problem with this model is...