Graphical models for multivariate time series is a concept extended by Dahlhaus (2000) from a random vector to a time series. We propose a test statistic to identify a graphical model for multivariate time series with the Kullback-Leibler distance between two spectral density matrices characterized by graphical models. Asymptotic null distribution is derived to be normal with the mean and varia...

We introduce a new method of model order selection: minimum description complexity (MDC). The approach is motivated by the Kullback-Leibler information distance. The method suggests to choose the model set for which the "model set relative entropy" is minimum. The proposed method is comparable with the existing order estimation methods such as AIC and MDL. We elaborate on the advantages of MDC ...

We propose a new variational inference method based on a proximal framework that uses the Kullback-Leibler (KL) divergence as the proximal term. We make two contributions towards exploiting the geometry and structure of the variational bound. Firstly, we propose a KL proximal-point algorithm and show its equivalence to variational inference with natural gradients (e.g. stochastic variational in...

In this work, we apply the Bayesian approach for acoustic scattering problem to reconstruct shape of a sound-soft obstacle using limited-aperture far-field measure data. A novel total variation prior scheme is developed parameterization. It imposed on Fourier coefficients parameterization not itself. Using prior, some less smooth objects can be reconstructed. We also investigate well-posedness ...

Shannon information entropies in position and momentum spaces and their sum are calculated as functions of Z(2 < or = Z < or = 54) in atoms. Roothaan-Hartree-Fock electron wave functions are used. The universal property S = a + b ln Z is verified. In addition, we calculate the Kullback-Leibler relative entropy, the Jensen-Shannon divergence, Onicescu's information energy, and a complexity measu...