Evaluating Stan’s Variational Bayes Algorithm for Estimating Multidimensional IRT Models

The Added Value of Multidimensional IRT Models

Variational Bayes for Generic Topic Models

The article contributes a derivation of variational Bayes for a large class of topic models by generalising from the well-known model of latent Dirichlet allocation. For an abstraction of these models as systems of interconnected mixtures, variational update equations are obtained, leading to inference algorithms for models that so far have used Gibbs sampling exclusively.

Variational Bayes for generalized autoregressive models

We describe a variational Bayes (VB) learning algorithm for generalized autoregressive (GAR) models. The noise is modeled as a mixture of Gaussians rather than the usual single Gaussian. This allows different data points to be associated with different noise levels and effectively provides robust estimation of AR coefficients. The VB framework is used to prevent overfitting and provides model-o...

Variational Bayes for Hierarchical Mixture Models

In recent years, sparse classification problems have emerged in many fields of study. Finite mixture models have been developed to facilitate Bayesian inference where parameter sparsity is substantial. Classification with finite mixture models is based on the posterior expectation of latent indicator variables. These quantities are typically estimated using the expectation-maximization (EM) alg...

Bayesian Multidimensional IRT Models With a Hierarchical Structure

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