The χ principle and the unbiased predictive risk estimator are used to determine optimal regularization parameters in the context of 3D focusing gravity inversion with the minimum support stabilizer. At each iteration of the focusing inversion the minimum support stabilizer is determined and then the fidelity term is updated using the standard form transformation. Solution of the resulting Tikh...

This paper presents a Fisher information based Bayesian approach to analysis and design of the regularization and preconditioning parameters used with gradient based inverse scattering algorithms. In particular, a one-dimensional inverse problem is considered where the permittivity and conductivity profiles are unknown and the input data consist of the scattered field over a certain bandwidth. ...

This thesis addresses total variation (TV) image restoration and blind image deconvolution. Classical image processing problems, such as deblurring, call for some kind of regularization. Total variation is among the state-of-the-art regularizers, as it provides a good balance between the ability to describe piecewise smooth images and the complexity of the resulting algorithms. In this thesis, ...

‎the aim of this paper is to study distribution of ratios of generalized order statistics from pareto distribution. parameter estimation of pareto distribution based on generalized order statistics and ratios of them have been obtained. inferences using method of moments and unbiased estimator have been obtained to develop point estimations. consistency of unbiased estimator has been illustrate...

Policy gradient algorithms are widely used in reinforcement learning problems with continuous action spaces, which update the policy parameters along the steepest direction of the expected return. However, large variance of policy gradient estimation often causes instability of policy update. In this paper, we propose to suppress the variance of gradient estimation by directly employing the var...

In this paper we present an algorithm for parameterfree information-preserving surface restoration. The algorithm is designed for 2.5D and 3D surfaces. The basic idea is to extract noise and signal properties of the data simultaneously by variance-component estimation and use this information for ltering. The variance-component estimation delivers information on how to weigh the innuence of the...

In this paper, the regularized estimation of the displacement vector field (DVF) of a dynamic image sequence is considered. A new class of non-quadratic convex regularization functionals is employed to estimate the motion field in the presence of motion discontinuities and occlusions. The derivation of the functionals is based on entropy considerations and do not require parameter tuning as in ...

High dimension low sample size (HD-LSS) data are becoming increasingly present in a variety of fields, including chemometrics and medical imaging. Especially problems with n < p (more variables than measurements) present a challenge to data analysts since the classical techniques can not be used. In this paper, we consider HD-LSS data in regression parameter and covariance matrix estimation pro...

In this paper we present an algorithm for parameterfree information-preserving surface restoration. The algorithm is designed for 2.5D and 3D surfaces. The basic idea is to extract noise and signal properties of the data simultaneously by variance-component estimation and use this information for ltering. The variance-component estimation delivers information on how to weigh the innuence of the...

This contribution concerns a generalization of the Boltzmann Machine that allows us to use the learning rule for a much wider class of maximum likelihood and maximum a posteriori problems, including both supervised and unsupervised learning. Furthermore, the approach allows us to discuss regularization and generalization in the context of Boltzmann Machines. We provide an illustrative example c...