A well known identifiability issue in factor analytic models is the invariance with respect to
orthogonal transformations. This problem burdens the inference under a Bayesian setup …

Digital and wireless technology are a crucial part of today's modern life. Artificial intelligence
(AI) uses different technologies and systems for speech recognition, visual perception and …

Mixtures of factor analysers (MFA) models represent a popular tool for finding structure in
data, particularly high-dimensional data. While in most applications the number of clusters …

This research proposes a one-step Bayesian regularized approach to exploratory factor
analysis (EFA) with an unknown number of factors. The proposed Bayesian regularized …

We present Bayesian methods for estimating and selecting variables in a mixture of logistic
regression models. A common issue with the logistic model is its intractable likelihood …

We consider the problem of inferring an unknown number of clusters in multinomial count
data, by estimating finite mixtures of multinomial distributions with or without covariates. Both …

We propose a data-driven reversible jump (DDRJ) method for selecting and estimating a
mixture of regression models in a single run, which can also be applied as a robust …

Multivariate data is prevalent in modern applications, yet it often presents significant
analytical challenges. Factor models can offer an effective tool to address issues associated …