Finite mixtures of distributions have provided a mathematical-based approach to the statistical modeling of a wide variety of random phenomena. They underpin a variety of techniques in major areas of statistics, including cluster and latent class analyses, discriminant analysis, image analysis, and survival analysis, in addition to their more direct role in data analysis and inference of providing descriptive models for distributions
Mixture models occur in numerous settings including random and fixed effects models, clustering, dec...
We offer a brief review of the use of distributional mixture models with a finite number of componen...
Finite mixture (FM) models have become a standard tool to address research questions in different sc...
The important role of finite mixture models in the statistical analysis of data is underscored by th...
The important role of finite mixture models in the statistical analysis of data is underscored by th...
textabstractFinite mixture distributions are a weighted average of a ¯nite number of distributions. ...
Finite Mixture models are a state-of-the-art technique of segmentation. Next tosegmenting consumers ...
Mixture models have been around for over 150 years, and they are found in many branches of statistic...
Cluster analysis via a finite mixture model approach is considered. With this approach to clustering...
Finite mixture models are being commonly used in a wide range of applications in practice concerning...
Mixture models are widely used in statistical modeling since they can model situations which a simpl...
Clustering is a common and important issue, and finite mixture models based on the normal distributi...
Abstract. Finite mixture models can be used in estimating complex, unknown probability distributions...
Cluster analysis seeks to identify homogeneous subgroups of cases in a population. This article prov...
A natural Bayesian approach for mixture models with an unknown number of com-ponents is to take the ...
Mixture models occur in numerous settings including random and fixed effects models, clustering, dec...
We offer a brief review of the use of distributional mixture models with a finite number of componen...
Finite mixture (FM) models have become a standard tool to address research questions in different sc...
The important role of finite mixture models in the statistical analysis of data is underscored by th...
The important role of finite mixture models in the statistical analysis of data is underscored by th...
textabstractFinite mixture distributions are a weighted average of a ¯nite number of distributions. ...
Finite Mixture models are a state-of-the-art technique of segmentation. Next tosegmenting consumers ...
Mixture models have been around for over 150 years, and they are found in many branches of statistic...
Cluster analysis via a finite mixture model approach is considered. With this approach to clustering...
Finite mixture models are being commonly used in a wide range of applications in practice concerning...
Mixture models are widely used in statistical modeling since they can model situations which a simpl...
Clustering is a common and important issue, and finite mixture models based on the normal distributi...
Abstract. Finite mixture models can be used in estimating complex, unknown probability distributions...
Cluster analysis seeks to identify homogeneous subgroups of cases in a population. This article prov...
A natural Bayesian approach for mixture models with an unknown number of com-ponents is to take the ...
Mixture models occur in numerous settings including random and fixed effects models, clustering, dec...
We offer a brief review of the use of distributional mixture models with a finite number of componen...
Finite mixture (FM) models have become a standard tool to address research questions in different sc...
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