A matrix of discrimination measures (discrimination probabilities, numerical estimates of dissimilarity, etc.) satisfies Regular Minimality (RM) if every row and every column of the matrix contains a single minimal entry, and an entry minimal in its row is minimal in its column. We derive a formula for the proportion of RM-compliant matrices among all square matrices of a given size and with no tied entries. Under a certain "meta-probabilistic" model this proportion can be interpreted as the probability with which a randomly chosen matrix turns out to be RM-compliant
This monograph offers an invitation to the field of matrix concentration inequalities. It begins wit...
We consider a general class of random matrices whose entries are centred random variables, independe...
This thesis presents a probabilistic algorithm for the solution of system of homogeneous linear ineq...
For any given number of factors, Minimum Rank Factor Analysis yields optimal communalities for an ob...
We describe a principled way of imposing a metric representing dissimilarities on any discrete set o...
Books on linear models and multivariate analysis generally include a chapter on matrix algebra, quit...
During the last twenty years, Random matrix theory (RMT) has produced numerous results that allow a ...
We extend probability estimates on the smallest singular value of random matrices with independent e...
We investigate the problem of completing partial matrices to rank-1 probability matrices. The motiva...
We define the statistically strongly regular matrices analogous to the strongly regular matrices, an...
Description of modularity models used to test P-matrices without allometric size variation. May be u...
Cette thèse a pour principal objectif d'introduire des bases probabilistes tirées de la théorie de l...
Introducing a new method for studying general probability distributions on R^n, we generalize some r...
Let $M$ be a random $n\times n$ matrix with independent 0/1 random entries taking value 1 with prob...
Many researchers and authors have studied the distributions of the condition numbers of real Gaussia...
This monograph offers an invitation to the field of matrix concentration inequalities. It begins wit...
We consider a general class of random matrices whose entries are centred random variables, independe...
This thesis presents a probabilistic algorithm for the solution of system of homogeneous linear ineq...
For any given number of factors, Minimum Rank Factor Analysis yields optimal communalities for an ob...
We describe a principled way of imposing a metric representing dissimilarities on any discrete set o...
Books on linear models and multivariate analysis generally include a chapter on matrix algebra, quit...
During the last twenty years, Random matrix theory (RMT) has produced numerous results that allow a ...
We extend probability estimates on the smallest singular value of random matrices with independent e...
We investigate the problem of completing partial matrices to rank-1 probability matrices. The motiva...
We define the statistically strongly regular matrices analogous to the strongly regular matrices, an...
Description of modularity models used to test P-matrices without allometric size variation. May be u...
Cette thèse a pour principal objectif d'introduire des bases probabilistes tirées de la théorie de l...
Introducing a new method for studying general probability distributions on R^n, we generalize some r...
Let $M$ be a random $n\times n$ matrix with independent 0/1 random entries taking value 1 with prob...
Many researchers and authors have studied the distributions of the condition numbers of real Gaussia...
This monograph offers an invitation to the field of matrix concentration inequalities. It begins wit...
We consider a general class of random matrices whose entries are centred random variables, independe...
This thesis presents a probabilistic algorithm for the solution of system of homogeneous linear ineq...