The natural habitat of most Bayesian methods is data represented by exchangeable
sequences of observations, for which de Finetti's theorem provides the theoretical …

Whether the goal is to estimate the number of people that live in a congressional district, to
estimate the number of individuals that have died in an armed conflict, or to disambiguate …

We review inference under models with nonparametric Bayesian (BNP) priors. The
discussion follows a set of examples for some common inference problems. The examples …

Single-cell DNA sequencing has great potential to reveal the clonal genotypes and
population structure of human cancers. However, single-cell data suffer from missing values …

The classical mixture of Gaussians model is related to K-means via small-variance
asymptotics: as the covariances of the Gaussians tend to zero, the negative log-likelihood of …

Discrete random structures are important tools in Bayesian nonparametrics and the resulting
models have proven effective in density estimation, clustering, topic modeling and …

Objectives Juvenile idiopathic arthritis (JIA) is a common chronic childhood disease and
chronic pain is a debilitating feature. A strong link has been shown between poor sleep and …

The Poisson Binomial Distribution-Old & New Page 1 Statistical Science 2023, Vol. 38, No. 1,
108–119 https://doi.org/10.1214/22-STS852 © Institute of Mathematical Statistics, 2023 The …

We discuss the use of the determinantal point process (DPP) as a prior for latent structure in
biomedical applications, where inference often centers on the interpretation of latent …

Statistical latent feature models, such as latent factor models, are models where each
observation is associated with a vector of latent features. A general problem is how to select …