Community detection, a fundamental task for network analysis, aims to partition a network
into multiple sub-structures to help reveal their latent functions. Community detection has …

Decision tree ensembles are an extremely popular tool for obtaining high-quality predictions
in nonparametric regression problems. Unmodified, however, many commonly used …

Is infinity that far? A Bayesian nonparametric perspective of finite mixture models Page 1 The
Annals of Statistics 2022, Vol. 50, No. 5, 2641–2663 https://doi.org/10.1214/22-AOS2201 © …

Bayesian fractional posteriors Page 1 The Annals of Statistics 2019, Vol. 47, No. 1, 39–66
https://doi.org/10.1214/18-AOS1712 © Institute of Mathematical Statistics, 2019 BAYESIAN …

Dirichlet process mixtures are flexible nonparametric models, particularly suited to density
estimation and probabilistic clustering. In this work we study the posterior distribution …

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 …

In model-based clustering mixture models are used to group data points into clusters. A
useful concept introduced for Gaussian mixtures by Malsiner Walli et al.(Stat Comput 26 …

ABSTRACT A fundamental problem in network analysis is clustering the nodes into groups
which share a similar connectivity pattern. Existing algorithms for community detection …

Clustering is a fundamental tool for exploratory data analysis, and is ubiquitous across
scientific disciplines. Gaussian Mixture Model (GMM) is a popular probabilistic and …

Sparse sequences of neural spikes are posited to underlie aspects of working memory,
motor production, and learning. Discovering these sequences in an unsupervised manner is …