Add to ORKGDescription This package facilitates profile inference (inference at the posterior mode) for a class of product partition models (PPM). The Dirichlet process mixture is currently the only available member of this class. These methods search for the maximum posterior (MAP) estimate for the data partition in a PPM
Posterior classification table of the best-fitting latent process mixed model.</p
Dirichlet process (DP) mixture models are the cornerstone of nonparametric Bayesian statistics, and...
We describe and illustrate Bayesian inference in models for density estimation using mixtures of Dir...
Abstract The profdpm package facilitates inference at the posterior mode for a class of product part...
<p>Partitions and models for Maximum Likelihood (ML) and Bayesian inference based on PartitionFinder...
The Dirichlet process mixture (DPM) is a ubiquitous, flexible Bayesian nonparametric statistical mod...
Parametrically specified measurement and transition equations in State Space Models (SSM) are a sour...
Mixture models are ubiquitous in applied science. In many real-world applications, the number of mix...
Summary There has been increasing interest in applying Bayesian nonparametric methods in large sampl...
Bayesian nonparametric mixture models based on the Dirichlet process (DP) have been widely used for ...
The aim in this article is to provide a means to undertake Bayesian inference for mixture models whe...
The aim in this article is to provide a means to undertake Bayesian inference for mixture models whe...
The Dirichlet process mixtures (DPM) can automatically infer the model complexity from data. Hence i...
Abstract. We study the problem of testing the hypothesis that the mean vector of a random vector bel...
We investigate the relationships between Dirichlet process DP based models and allocation models fo...
Posterior classification table of the best-fitting latent process mixed model.</p
Dirichlet process (DP) mixture models are the cornerstone of nonparametric Bayesian statistics, and...
We describe and illustrate Bayesian inference in models for density estimation using mixtures of Dir...
Abstract The profdpm package facilitates inference at the posterior mode for a class of product part...
<p>Partitions and models for Maximum Likelihood (ML) and Bayesian inference based on PartitionFinder...
The Dirichlet process mixture (DPM) is a ubiquitous, flexible Bayesian nonparametric statistical mod...
Parametrically specified measurement and transition equations in State Space Models (SSM) are a sour...
Mixture models are ubiquitous in applied science. In many real-world applications, the number of mix...
Summary There has been increasing interest in applying Bayesian nonparametric methods in large sampl...
Bayesian nonparametric mixture models based on the Dirichlet process (DP) have been widely used for ...
The aim in this article is to provide a means to undertake Bayesian inference for mixture models whe...
The aim in this article is to provide a means to undertake Bayesian inference for mixture models whe...
The Dirichlet process mixtures (DPM) can automatically infer the model complexity from data. Hence i...
Abstract. We study the problem of testing the hypothesis that the mean vector of a random vector bel...
We investigate the relationships between Dirichlet process DP based models and allocation models fo...
Posterior classification table of the best-fitting latent process mixed model.</p
Dirichlet process (DP) mixture models are the cornerstone of nonparametric Bayesian statistics, and...
We describe and illustrate Bayesian inference in models for density estimation using mixtures of Dir...