Nonnegative matrix factorization (NMF) has become a widely used tool for the analysis of
high-dimensional data as it automatically extracts sparse and meaningful features from a set …

How does an algebraic geometer studying secant varieties further the understanding of
hypothesis tests in statistics? Why would a statistician working on factor analysis raise open …

The quantitative analysis of biological sequence data is based on methods from statistics
coupled with efficient algorithms from computer science. Algebra provides a framework for …

Identifying the underlying structure of a data set and extracting meaningful information is a
key problem in data analysis. Simple and powerful methods to achieve this goal are linear …

Bayesian decision analysis supports principled decision making in complex domains. This
textbook takes the reader from a formal analysis of simple decision problems to a careful …

Written by some major contributors to the development of this class of graphical models,
Chain Event Graphs introduces a viable and straightforward new tool for statistical inference …

Abstract Let M ∈ R^ p * q M∈ R p× q be a nonnegative matrix. The positive semidefinite
rank (psd rank) of M is the smallest integer k for which there exist positive semidefinite …

Maximum likelihood estimation (MLE) is a fundamental computational problem in statistics,
and it has recently been studied with some success from the perspective of algebraic …

In this paper, we introduce and provide a short overview of nonnegative matrix factorization
(NMF). Several aspects of NMF are discussed, namely, the application in hyperspectral …

Algebraic tools have been used in statistical research since the very beginning of the field. In
recent years, the interaction between statistics and pure mathematics has intensified and …