The BayesLCA package for R provides tools for performing latent class analysis within a Bayesian setting. Three methods for fitting the model are provided, incorporating an expectation-maximization algorithm, Gibbs sampling and a variational Bayes approximation. The article briefly outlines the methodology behind each of these techniques and discusses some of the technical difficulties associated with them. Methods to remedy these problems are also described. Visualization methods for each of these techniques are included, as well as criteria to aid model selection.Science Foundation Irelan
The Bayesian researcher should know the basic ideas underlying Bayesian methodology and the computat...
BayesClass implements ten algorithms for learning Bayesian network classifiers from discrete data. T...
In a latent class IRT model in which the latent classes are ordered on one dimension, the class spe-...
The BayesLCA package for R provides tools for performing latent class analysis within a Bayesian set...
The BayesLCA package for R provides tools for performing latent class analysis within a Bayesian set...
There is an explosion of interest in Bayesian statistics, primarily because recently created computa...
International audienceThis Bayesian modeling book provides a self-contained entry to computational B...
This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Fo...
R topics documented: BayesLCA-package..................................... 2 Alzheimer.................
Background: Many recent statistical applications involve inference under complex models, where it is...
Compared with traditional statistics, only a few social scientists employ Bayesian analyses. The exi...
Latent class is a method for classifying subjects, originally based on binary outcome data but now e...
There has been a dramatic growth in the development and application of Bayesian inferential methods....
This Bayesian modeling book is intended for practitioners and applied statisticians looking for a se...
The exponential growth of social data both in volume and complexity has increasingly exposed many of...
The Bayesian researcher should know the basic ideas underlying Bayesian methodology and the computat...
BayesClass implements ten algorithms for learning Bayesian network classifiers from discrete data. T...
In a latent class IRT model in which the latent classes are ordered on one dimension, the class spe-...
The BayesLCA package for R provides tools for performing latent class analysis within a Bayesian set...
The BayesLCA package for R provides tools for performing latent class analysis within a Bayesian set...
There is an explosion of interest in Bayesian statistics, primarily because recently created computa...
International audienceThis Bayesian modeling book provides a self-contained entry to computational B...
This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Fo...
R topics documented: BayesLCA-package..................................... 2 Alzheimer.................
Background: Many recent statistical applications involve inference under complex models, where it is...
Compared with traditional statistics, only a few social scientists employ Bayesian analyses. The exi...
Latent class is a method for classifying subjects, originally based on binary outcome data but now e...
There has been a dramatic growth in the development and application of Bayesian inferential methods....
This Bayesian modeling book is intended for practitioners and applied statisticians looking for a se...
The exponential growth of social data both in volume and complexity has increasingly exposed many of...
The Bayesian researcher should know the basic ideas underlying Bayesian methodology and the computat...
BayesClass implements ten algorithms for learning Bayesian network classifiers from discrete data. T...
In a latent class IRT model in which the latent classes are ordered on one dimension, the class spe-...