Optimal transport and Wasserstein distances are flourishing in many scientific fields as a
means for comparing and connecting random structures. Here we pioneer the use of an …

The use of hierarchical mixture priors with shared atoms has recently flourished in the
Bayesian literature for partially exchangeable data. Leveraging on nested levels of mixtures …

For exchangeable data, mixture models are an extremely useful tool for density estimation
due to their attractive balance between smoothness and flexibility. When additional …

Overweight and obesity in adults are known to be associated with increased risk of
metabolic and cardiovascular diseases. Obesity has now reached epidemic proportions …

Functional Concurrent Regression Mixture Models Using Spiked Ewens-Pitman Attraction Priors
Page 1 Bayesian Analysis (2024) 19, Number 4, pp. 1067–1095 Functional Concurrent …

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 …

Previous work constructed Fleming–Viot-type measure-valued diffusions (and diffusions on
a space of interval partitions of the unit interval $[0, 1] $) that are stationary with respect to …

Modelling of the dependence structure across heterogeneous data is crucial for Bayesian
inference, since it directly impacts the borrowing of information. Despite extensive advances …

The results of some divisions related to Brexit held in the House of Commons are
investigated. In particular, a new class of mixture models with concomitant covariates is …