As power systems evolve by integrating renewable energy sources, distributed generation,
and electric vehicles, the complexity of managing these systems increases. With the …

We propose a randomized greedy search algorithm to find a point estimate for a random
partition based on a loss function and posterior Monte Carlo samples. Given the large size …

Entity resolution is the process of merging and removing duplicate records from multiple
data sources, often in the absence of unique identifiers. Bayesian models for entity …

This chapter presents some of the Bayesian solutions to the different interpretations of
picking the “right” number of components in a mixture, before concluding on the ill-posed …

Bayesian mixture models are widely used for clustering of high-dimensional data with
appropriate uncertainty quantification. However, as the dimension of the observations …

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

BNPmix is an R package for Bayesian nonparametric multivariate density estimation,
clustering, and regression, using Pitman-Yor mixture models, a flexible and robust …

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

This chapter introduces the model-based clustering is related to standard heuristic clustering
methods and an overview of different ways to specify the cluster model. It provides the …

This article introduces a new class of models for multiple networks. The core idea is to
parameterize a distribution on labeled graphs in terms of a Fréchet mean graph (which …