In this paper we address the problem of multi-modal coregistration of medical 3D images. Several techniques for the rigid registration of multi-modal images have been developed; in one of those the Kullback-Leibler distance is used to align 2D-3D angiographic images [1]. In this paper we refine this technique and investigate its performance on the registration of pairs of 3D CT/US images. We te...

Stochastic interdependence of a probablility distribution on a product space is measured by its Kullback-Leibler distance from the exponential family of product distributions (called multi-information). Here we investigate lowdimensional exponential families that contain the maximizers of stochastic interdependence in their closure. Based on a detailed description of the structure of probablili...

Regularized (stabilized) versions of exponential and linear forgetting in parameter tracking are shown to be dual to each other. Both are derived by solving essentially the same Bayesian decision-making problem where Kullback-Leibler divergence is used to measure (quasi)distance between posterior probability distributions of estimated parameters. The type of forgetting depends solely on the ord...

A new method for automatically acquiring grammar fragments for understanding uently spoken language is proposed. The goal of this method is to generate a collection of grammar fragments each representing a set of syntactically and semantically similar phrases. First phrases observed frequently in the training set are selected as candidates. Each candidate phrase has three associated probability...

Kullback-Leibler divergence is a natural distance measure between two probabilistic finite-state automata. Computing this distance is difficult, since it requires a summation over a countably infinite number of strings. Nederhof and Satta (2004) recently provided a solution in the course of solving the more general problem of finding the cross-entropy between a probabilistic context-free gramma...

We introduce a Kullback-Leibler type distance between spectral density functions of stationary stochastic processes and solve the problem of optimal approximation of a given spectral density Ψ by one that is consistent with prescribed second-order statistics. In general, such statistics are expressed as the state covariance of a linear filter driven by a stochastic process whose spectral densit...

Model or variable selection is usually achieved through ranking models according to the increasing order of preference. One of methods is applying Kullback-Leibler distance or relative entropy as a selection criterion. Yet that will raise two questions, why uses this criterion and are there any other criteria. Besides, conventional approaches require a reference prior, which is usually difficul...

In this paper we intend to examine the application of Kullback-Leibler, Hellinger and LINEX loss function in Dynamic Linear Model using the real price of oil for 106 years of data from 1913 to 2018 concerning the asymmetric problem in filtering and forecasting. We use DLM form of the basic Hoteling Model under Quadratic loss function, Kullback-Leibler, Hellinger and LINEX trying to address the ...