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

Nonparametric Bayesian methods are considered for recovery of imagery based upon
compressive, incomplete, and/or noisy measurements. A truncated beta-Bernoulli process is …

In the superset label learning problem (SLL), each training instance provides a set of
candidate labels of which one is the true label of the instance. As in ordinary regression, the …

Clustering is an important task in many areas of knowledge: medicine and epidemiology,
genomics, environmental science, economics, visual sciences, among others …

Abstract Dirichlet Process Mixture (DPM) models have been increasingly employed to
specify random partition models that take into account possible patterns within covariates …

Flexible covariate-dependent density estimation can be achieved by modelling the joint
density of the response and covariates as a Dirichlet process mixture. An appealing aspect …

We present the discrete infinite logistic normal distribution (DILN,“Dylan”), a Bayesian
nonparametric prior for mixed membership models. DILN is a generalization of the …

This paper considers the problem of estimating multiple frequency hopping signals with
unknown hopping pattern. By segmenting the received signals into overlapped …

Variational Bayes (VB) is a common strategy for approximate Bayesian inference, but simple
methods are only available for specific classes of models including, in particular …

Spatial Product Partition Models Page 1 Bayesian Analysis (2016) 11, Number 1, pp. 265–298
Spatial Product Partition Models ∗ Garritt L. Page † and Fernando A. Quintana ‡ Abstract …