Bayesian Variable Selection for Latent Class Models

Bayesian variable selection for latent class models.

In this article, we develop a latent class model with class probabilities that depend on subject-specific covariates. One of our major goals is to identify important predictors of latent classes. We consider methodology that allows estimation of latent classes while allowing for variable selection uncertainty. We propose a Bayesian variable selection approach and implement a stochastic search G...

Latent Class Analysis Variable Selection.

We propose a method for selecting variables in latent class analysis, which is the most common model-based clustering method for discrete data. The method assesses a variable's usefulness for clustering by comparing two models, given the clustering variables already selected. In one model the variable contributes information about cluster allocation beyond that contained in the already selected...

Bayesian inference for latent variable models

3 Abstract Bayes’ theorem is the cornerstone of statistical inference. It provides the tools for dealing with knowledge in an uncertain world, allowing us to explain observed phenomena through the refinement of belief in model parameters. At the heart of this elegant framework lie intractable integrals, whether in computing an average over some posterior distribution, or in determining the norm...

Bayesian learning of latent variable models

Bayesian Variable Selection for Probit Mixed Models

In computational biology, gene expression datasets are characterized by very few individual samples compared to a large number of measurments per sample. Thus, it is appealing to merge these datasets in order to increase the number of observations and diversify the data, allowing a more reliable selection of genes relevant to the biological problem. This necessitates the introduction of the dat...