The proposal and study of dependent Bayesian nonparametric models has been one of the
most active research lines in the last two decades, with random vectors of measures …

A new class of dependent random measures which we call compound random measures is
proposed and the use of normalized versions of these random measures as priors in …

We propose a novel statistical model for sparse networks with overlapping community
structure. The model is based on representing the graph as an exchangeable point process …

Sparse nonnegative matrix factorization (SNMF) aims to factorize a data matrix into two
optimized nonnegative sparse factor matrices, which could benefit many tasks, such as …

We propose a novel statistical model for sparse networks with overlapping community
structure. The model is based on representing the graph as an exchangeable point process …

In many real problems, dependence structures more general than exchangeability are
required. For instance, in some settings partial exchangeability is a more reasonable …

Modelling and Computation Using NCoRM Mixtures for Density Regression Page 1 Bayesian
Analysis (2018) 13, Number 3, pp. 897–916 Modelling and Computation Using NCoRM Mixtures …

Developing transparent predictive analytics has attracted significant research attention
recently. There have been multiple theories on how to model learning transparency but …

In the big data era there is a growing need to model the main features of large and non-
trivial data sets. This paper proposes a Bayesian nonparametric prior for modelling …

Deciphering latent structure in data is one of the fundamental challenges that the machine
learning community has been grappling with in recent years. Developments in non …