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 …

Bayesian nonparametric mixture models are common for modeling complex data. While
these models are well‐suited for density estimation, recent results proved posterior …

Modeling species distributions over space and time is one of the major research topics in
both ecology and conservation biology. Joint Species Distribution models (JSDMs) have …

Inference for Bayesian Nonparametric Models with Binary Response Data via Permutation
Counting Page 1 Bayesian Analysis (2024) 19, Number 1, pp. 293–318 Inference for Bayesian …

The study of almost surely discrete random probability measures is an active line of research
in Bayesian nonparametrics. The idea of assuming interaction across the atoms of the …

Independent Finite Approximations for Bayesian Nonparametric Inference Page 1 Bayesian
Analysis (2024) 19, Number 4, pp. 1187–1224 Independent Finite Approximations for …

We propose a novel model-based clustering approach for samples of time series. We
assume as a unique commonality that two observations belong to the same group if …

In this work, we present a comprehensive Bayesian posterior analysis of what we term
Poisson Hierarchical Indian Buffet Processes, designed for complex random sparse count …

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 …

In Bayesian nonparametrics, knowledge of the prior distribution induced on the number of
clusters is key for prior specification and calibration. However, evaluating this prior is …