Standard regression approaches assume that some finite number of the response
distribution characteristics, such as location and scale, change as a (parametric or …

Bayesian cluster analysis offers substantial benefits over algorithmic approaches by
providing not only point estimates but also uncertainty in the clustering structure and …

Clustering is an important task in many areas of knowledge: medicine and epidemiology,
genomics, environmental science, economics, visual sciences, among others …

The Bayesian approach to inference stands out for naturally allowing borrowing information
across heterogeneous populations, with different samples possibly sharing the same …

The use of large datasets for targeted therapeutic interventions requires new ways to
characterize the heterogeneity observed across subgroups of a specific population. In …

Standard clustering techniques assume a common configuration for all features in a dataset.
However, when dealing with multi-view or longitudinal data, the clusters' number …

Mouse-tracking data, which record computer mouse trajectories while participants perform
an experimental task, provide valuable insights into subjects' underlying cognitive …

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 …

Model selection for maternal hypertensive disorders with symmetric hierarchical Dirichlet
processes Page 1 The Annals of Applied Statistics 2023, Vol. 17, No. 1, 313–332 https://doi.org/10.1214/22-AOAS1628 …

We propose a methodology for modelling and comparing probability distributions within a
Bayesian nonparametric framework. Building on dependent normalised random measures …