Flexible modeling of how an entire distribution changes with covariates is an important yet
challenging generalization of mean-based regression that has seen growing interest over …

The prior distribution is the usual starting point for Bayesian uncertainty. In this paper, we
present a different perspective that focuses on missing observations as the source of …

I congratulate the authors on an interesting article and thank the
Editorsfortheopportunitytodiscussthework. Themanuscript under discussion devises an …

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

The proposal and study of dependent Bayesian nonparametric models has been one of the
most active research lines in the last two decades, with random vectors of measures …

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 …

Loss-based clustering methods, such as k-means clustering and its variants, are standard
tools for finding groups in data. However, the lack of quantification of uncertainty in the …

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 …

Several epidemiological studies have provided evidence that long-term exposure to fine
particulate matter (pm2. 5) increases mortality rate. Furthermore, some population …

We argue for the use of separate exchangeability as a modeling principle in Bayesian
inference, especially for nonparametric Bayesian models. While in some areas, such as …