Matrices with random (or pseudo-random) elements appear naturally in different physical problems and their statistical properties have been thoroughly investigated (see e.g. [1]). A special case of random matrices, called distance matrices, has been recently proposed in [2]. They are defined for any metric space X with a probability measure μ on it as follows. Choose N points ~xj ∈ X randomly d...

A Wishart model is proposed for random distance matrices in which the components are correlated gamma random variables, all having the same degrees of freedom. The marginal likelihood is obtained in closed form. Its use is illustrated by multidimensional scaling, by rooted tree models for response covariances in social survey work, and unrooted trees for ancestral relationships in genetic appli...

Distance matrices are matrices whose elements are the relative distances between points located on a certain manifold. In all cases considered here all their eigenvalues except one are non-positive. When the points are uncorrelated and randomly distributed we investigate the average density of their eigenvalues and the structure of their eigenfunctions. The spectrum exhibits delocalized and str...

We consider distance matrices of certain graphs and of points chosen in a rectangular grid. Formulae for the inverse and the determinant of the distance matrix of a weighted tree are obtained. Results concerning the inertia and the determinant of the distance matrix of an unweighted unicyclic graph are proved. If D is the distance matrix of a tree, then we obtain certain results for a perturbat...

Over the past decade, Euclidean distance matrices, or EDMs, have been receiving increased attention for two main reasons. The first reason is that the many applications of EDMs, such as molecular conformation in bioinformatics, dimensionality reduction in machine learning and statistics, and especially the problem of wireless sensor network localization, have all become very active areas of res...