Statistical network modelling has focused on representing the graph as a discrete structure,
namely the adjacency matrix. When assuming exchangeability of this array—which can aid …

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

Nonparametric Bayesian (BNP) inference is concerned with inference for infinite
dimensional parameters, including unknown distributions, families of distributions, random …

Truncated random measures Page 1 Bernoulli 25(2), 2019, 1256–1288 https://doi.org/10.3150/18-BEJ1020
Truncated random measures TREVOR CAMPBELL1,*, JONATHAN H. HUGGINS1 …

Completely random measures (CRM) represent the key building block of a wide variety of
popular stochastic models and play a pivotal role in modern Bayesian Nonparametrics. The …

This paper adopts a Bayesian nonparametric mixture model where the mixing distribution
belongs to the wide class of normalized homogeneous completely random measures. We …

Precision medicine is an approach for disease treatment that defines treatment strategies
based on the individual characteristics of the patients. Motivated by an open problem in …

Low Information Omnibus (LIO) Priors for Dirichlet Process Mixture Models Page 1 Bayesian
Analysis (2019) 14, Number 3, pp. 677–702 Low Information Omnibus (LIO) Priors for Dirichlet …

The Dirichlet process was introduced by Ferguson in 1973 to use with Bayesian
nonparametric inference problems. A lot of work has been done based on the Dirichlet …

Motivated by the problem of accurately predicting gap times between successive blood
donations, we present here a general class of Bayesian nonparametric models for …