Understanding knowledge representations in neural nets has been a difficult problem. Principal components analysis (PCA) of contributions (products of sending activations and connection weights) has yielded valuable insights into knowledge representations, but much of this work has focused on the correlation matrix of contributions. The present work shows that analyzing the variance-covariance ...

Most eigenstructure-based blind channel identification and equalization algorithms with second-order statistics need SVD or EVD of the correlation matrix of the received signal. In this paper, we address new algorithms based on QR factorization of the received signal directly without calculating the correlation matrix. This renders the QR factorization-based algorithms more robust against ill-c...

This article discusses aeroacoustic imaging methods based on correlation measurements in the frequency domain. Standard this field assume that estimated matrix is superimposed with additive white noise. In paper we present a mathematical model for measurement process covering arbitrarily correlated The covariance of data given terms fourth order moments. aim to explore use such additional infor...

The covariance matrix plays an important role in statistical inference, yet modeling a covariance matrix is often a difficult task in practice due to its dimensionality and the non-negative definite constraint. In order to model a covariance matrix effectively, it is typically broken down into components based on modeling considerations or mathematical convenience. Decompositions that have rece...

New dynamic models for realized covariance matrices are proposed. The expected value of the realized covariance matrix is specified in two steps: a model for each realized variance, and a model for the realized correlation matrix. The realized correlation model is a dynamic conditional correlation model. Estimation can be done in two steps as well, and a QML interpretation is given to each step...

We provide sufficient conditions of Pólya type which guarantee the positive definiteness an isotropic 2 × 2-matrix-valued function in R and R3. Several bivariate covariance models have been proposed literature, where all components matrix are same parametric family, such as Matérn model. Based on conditions, we introduce two novel this class, powered exponential (or stable) model generalized Ca...