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 …

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

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 …

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 …

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 …

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 …

Exchangeability, Prediction and Predictive Modeling in Bayesian Statistics Page 1 Statistical
Science 2025, Vol. 40, No. 1, 40–67 https://doi.org/10.1214/24-STS965 © Institute of …