Standard regression approaches assume that some finite number of the response
distribution characteristics, such as location and scale, change as a (parametric or …

Bayesian Cluster Analysis: Point Estimation and Credible Balls (with Discussion) Page 1
Bayesian Analysis (2018) 13, Number 2, pp. 559–626 Bayesian Cluster Analysis: Point …

We review inference under models with nonparametric Bayesian (BNP) priors. The
discussion follows a set of examples for some common inference problems. The examples …

This paper concerns the use of Markov chain Monte Carlo methods for posterior sampling in
Bayesian nonparametric mixture models with normalized random measure priors. Making …

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

A review of uncertainty quantification for density estimation Page 1 Statistics Surveys Vol. 15
(2021) 1–71 ISSN: 1935-7516 https://doi.org/10.1214/21-SS130 A review of uncertainty …

Sequential search models are popular in marketing for studying consumer search behavior.
Current search models use parametric assumptions regarding different aspects of the …

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 …

In this work we propose a model-based clustering method for time series. The model uses
an almost surely discrete Bayesian nonparametric prior to induce clustering of the series …

Nonparametric Bayesian (BNP) inference is concerned with inference for infinite
dimensional parameters, including unknown distributions, families of distributions, random …