Abstract The profdpm package facilitates inference at the posterior mode for a class of product partition models (PPM). Dirichlet process mixtures are currently the only available class members. Several methods are implemented to search for the maximum posterior estimate of the data partition. This article discusses the relevant theory, the R and underlying C implementation, and examples of high level functionality
Mixture models are ubiquitous in applied science. In many real-world applications, the number of mix...
Panel data econometrics is obviously one of the main fields in the profession, but most of the model...
<p>Partitions and models for Maximum Likelihood (ML) and Bayesian inference based on PartitionFinder...
The profdpm package facilitates inference at the posterior mode for a class of product partition mod...
Description This package facilitates profile inference (inference at the posterior mode) for a class...
The Dirichlet process mixture (DPM) is a ubiquitous, flexible Bayesian nonparametric statistical mod...
Summary There has been increasing interest in applying Bayesian nonparametric methods in large sampl...
Parametrically specified measurement and transition equations in State Space Models (SSM) are a sour...
In the Bayesian mixture modeling framework it is possible to infer the necessary number of component...
In Bayesian probability theory, if the posterior distributions p(θ|x) are in the same family as the ...
BNPmix is an R package for Bayesian nonparametric multivariate density estimation, clustering, and r...
This vignette is published as Kohl and Ruckdeschel (2010c). Package distrMod pro-vides an object ori...
We investigate the relationships between Dirichlet process DP based models and allocation models fo...
This package provides a set of methods for estimation and statistical inference on the solutions (op...
depmixS4 implements a general framework for defining and estimating dependent mixture models in the ...
Mixture models are ubiquitous in applied science. In many real-world applications, the number of mix...
Panel data econometrics is obviously one of the main fields in the profession, but most of the model...
<p>Partitions and models for Maximum Likelihood (ML) and Bayesian inference based on PartitionFinder...
The profdpm package facilitates inference at the posterior mode for a class of product partition mod...
Description This package facilitates profile inference (inference at the posterior mode) for a class...
The Dirichlet process mixture (DPM) is a ubiquitous, flexible Bayesian nonparametric statistical mod...
Summary There has been increasing interest in applying Bayesian nonparametric methods in large sampl...
Parametrically specified measurement and transition equations in State Space Models (SSM) are a sour...
In the Bayesian mixture modeling framework it is possible to infer the necessary number of component...
In Bayesian probability theory, if the posterior distributions p(θ|x) are in the same family as the ...
BNPmix is an R package for Bayesian nonparametric multivariate density estimation, clustering, and r...
This vignette is published as Kohl and Ruckdeschel (2010c). Package distrMod pro-vides an object ori...
We investigate the relationships between Dirichlet process DP based models and allocation models fo...
This package provides a set of methods for estimation and statistical inference on the solutions (op...
depmixS4 implements a general framework for defining and estimating dependent mixture models in the ...
Mixture models are ubiquitous in applied science. In many real-world applications, the number of mix...
Panel data econometrics is obviously one of the main fields in the profession, but most of the model...
<p>Partitions and models for Maximum Likelihood (ML) and Bayesian inference based on PartitionFinder...
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