In this paper, we start by reviewing exchangeability and its relevance to the Bayesian
approach. We highlight the predictive nature of Bayesian models and the symmetry …

Distribution theory for hierarchical processes Page 1 The Annals of Statistics 2019, Vol. 47, No.
1, 67–92 https://doi.org/10.1214/17-AOS1678 © Institute of Mathematical Statistics, 2019 …

Assessing homogeneity of distributions is an old problem that has received considerable
attention, especially in the nonparametric Bayesian literature. To this effect, we propose the …

In this article, we propose a Bayesian nonparametric model for clustering grouped data. We
adopt a hierarchical approach: at the highest level, each group of data is modeled according …

Supplementary material A to Hierarchical Species Sampling Models. This document
contains the derivations of the results of the paper and a detailed analysis of the generalized …

Posterior computation in hierarchical Dirichlet process (HDP) mixture models is an active
area of research in nonparametric Bayes inference of grouped data. Existing literature …

Bayesian hierarchical models are used to share information between related samples and to
obtain more accurate estimates of sample level parameters, common structure and variation …

Survival analysis via hierarchically dependent mixture hazards Page 1 The Annals of Statistics
2021, Vol. 49, No. 2, 863–884 https://doi.org/10.1214/20-AOS1982 © Institute of Mathematical …

Hierarchical models enjoy great popularity due to their ability to handle heterogeneous
groups of observations by leveraging on their underlying common structure. In a Bayesian …

Bayesian hierarchical models are powerful tools for learning common latent features across
multiple data sources. The Hierarchical Dirichlet Process (HDP) is invoked when the number …