Bayesian nonparametric mixture models are often employed for modelling complex data. While these models are well-suited for density estimation, their application for clustering has some limitations. Miller and Harrison (2014) proved posterior inconsistency in the number of clusters when the true number of clusters is finite for Dirichlet process and Pitman–Yor process mixture models. In this work, we extend thisresult to additional Bayesian nonparametric priors such as Gibbs-type processes and finite-dimensional representations of them. The latter include the Dirichlet multinomial process and the recently emerged Pitman–Yor and normalized generalized gamma multinomial processes. We show that mixture models based on these processes are also ...
Although Bayesian nonparametric mixture models for continuous data are well developed, the literatur...
Discrete nonparametric priors play a central role in a variety of Bayesian procedures, most notably ...
This dissertation explores a Bayesian nonparametric approach to mixture modeling and the use of the ...
Bayesian nonparametric mixture models are common for modeling complex data. While these models are w...
A useful step in data analysis is clustering, in which observations are grouped together in a hopefu...
In many applications, a finite mixture is a natural model, but it can be difficult to choose an appr...
The paper deals with the problem of determining the number of components in a mixture model. We take...
International audienceIn Bayesian nonparametrics, knowledge of the prior distribution induced on the...
The past decade has seen a remarkable development in the area of Bayesian nonparametric inference fr...
In recent years, there has been increasing interest in Bayesian nonparametric methods for high-dimen...
In the Bayesian nonparametric family, Dirichlet Process (DP) is a prior distribution that is able to...
The parsimonious Gaussian mixture models, which exploit an eigenvalue decomposition of the group cov...
The past decade has seen a remarkable development in the area of Bayesian nonparametric inference fr...
Data clustering is a fundamental unsupervised learning approach that impacts several domains such as...
Mixture models are one of the most widely used statistical tools when dealing with data from heterog...
Although Bayesian nonparametric mixture models for continuous data are well developed, the literatur...
Discrete nonparametric priors play a central role in a variety of Bayesian procedures, most notably ...
This dissertation explores a Bayesian nonparametric approach to mixture modeling and the use of the ...
Bayesian nonparametric mixture models are common for modeling complex data. While these models are w...
A useful step in data analysis is clustering, in which observations are grouped together in a hopefu...
In many applications, a finite mixture is a natural model, but it can be difficult to choose an appr...
The paper deals with the problem of determining the number of components in a mixture model. We take...
International audienceIn Bayesian nonparametrics, knowledge of the prior distribution induced on the...
The past decade has seen a remarkable development in the area of Bayesian nonparametric inference fr...
In recent years, there has been increasing interest in Bayesian nonparametric methods for high-dimen...
In the Bayesian nonparametric family, Dirichlet Process (DP) is a prior distribution that is able to...
The parsimonious Gaussian mixture models, which exploit an eigenvalue decomposition of the group cov...
The past decade has seen a remarkable development in the area of Bayesian nonparametric inference fr...
Data clustering is a fundamental unsupervised learning approach that impacts several domains such as...
Mixture models are one of the most widely used statistical tools when dealing with data from heterog...
Although Bayesian nonparametric mixture models for continuous data are well developed, the literatur...
Discrete nonparametric priors play a central role in a variety of Bayesian procedures, most notably ...
This dissertation explores a Bayesian nonparametric approach to mixture modeling and the use of the ...