Graphs representing complex systems often share a partial underlying structure across
domains while retaining individual features. Thus, identifying common structures can shed …

Generalised Bayesian inference updates prior beliefs using a loss function, rather than a
likelihood, and can therefore be used to confer robustness against possible mis …

Undirected graphical models, also known as Markov random fields, have become a popular
tool for representing network structure of high-dimensional data in a large variety of areas …

Discrete state spaces represent a major computational challenge to statistical inference,
since the computation of normalization constants requires summation over large or possibly …

A common challenge in estimating parameters of probability density functions is the
intractability of the normalizing constant. While in such cases maximum likelihood estimation …

Classical change detection algorithms typically require modeling pre-change and post-
change distributions. The calculations may not be feasible for various machine learning …

Estimation of density functions supported on general domains arises when the data are
naturally restricted to a proper subset of the real space. This problem is complicated by …

Unnormalized statistical models play an important role in machine learning, statistics, and
signal processing. In this paper, we derive a new hypothesis testing procedure for …

Probabilistic graphical models provide a flexible yet parsimonious framework for modeling
dependencies among nodes in networks. There is a vast literature on parameter estimation …

Angular measurements are often modeled as circular random variables, where there are
natural circular analogues of moments, including correlation. Because a product of circles is …