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 …

Heavy tails are often found in practice, and yet they are an Achilles heel of a variety of
mainstream random probability measures such as the Dirichlet process (DP). The first …

Our object of study is the general class of stick-breaking processes with exchangeable
length variables. These generalize well-known Bayesian nonparametric priors in an …

Motivated by the Hopfield's network, a conditional maximization routine is used in order to
compute the posterior mode of a random allocation model. The proposed approach applies …

Modelling of the dependence structure across heterogeneous data is crucial for Bayesian
inference, since it directly impacts the borrowing of information. Despite extensive advances …

One of the central issues in statistics and machine learning is how to select an adequate
model that can automatically adapt its complexity to the observed data. In the present paper …

An unsupervised classification method for point events occurring on a geometric network is
proposed. The idea relies on the distributional flexibility and practicality of random partition …

Random discrete distributions, say $ F, $ known as species sampling models, represent a
rich class of models for classification and clustering, in Bayesian statistics and machine …

Large classes of problems can be formulated as inverse problems, where the goal is to find
parameter values that best explain some observed measures. The relationship between …

Gibbs sampling methods are standard tools to perform posterior inference for mixture
models. These have been broadly classified into two categories: marginal and conditional …