Matrix-variate distributions represent a natural way for modeling random matrices. Realizations from random matrices are generated by the simultaneous observation of variables in different situations or locations, and are commonly arranged in three-way data structures. Among the matrix-variate distributions, the matrix normal density plays the same pivotal role as the multivariate normal distri...

In this paper, elliptic symmetric matrix variate distribution is proposed as the prior distribution for maximum a posterior linear regression (MAPLR) based model adaptation. The exact close form solution of MAPLR with elliptically symmetric matrix variate priors is obtained. The effects of the proposed prior in MAPLR are characterized and compared with conventional maximum likelihood linear reg...

The theoretical basis and computational procedures for estimating the culling variate utilized by breeders in actual selection are presented. The essential part of this procedure is to derive the unknown culling variate in terms of a linear combination of traits based on realized indirect selection differentials of those traits and phenotypic variances and covariances among traits in the popula...

HFE BASED MULTI-VARIATE QUADRATIC CRYPTOSYSTEMS AND DEMBOWSKI OSTROM POLYNOMIALS Alam, Bilal Ph.D., Department of Cryptography Supervisor : Prof. Dr. Ferruh Özbudak Co-Supervisor : Dr. Oğuz Yayla

In this state-of-the-art report we discuss relevant research works related to the visualization of complex, multivariate data. We discuss how different techniques take effect at specific stages of the visualization pipeline and how they apply to multi-variate data sets being composed of scalars, vectors and tensors. We also provide a categorization of these techniques with the aim for a better ...

In this paper we propose a matrix-variate Dirichlet process (MATDP) for modeling the joint prior of a set of random matrices. Our approach is able to share statistical strength among regression coe cient matrices due to the clustering property of the Dirichlet process. Moreover, since the base probability measure is de ned as a matrix-variate distribution, the dependence among the elements of e...