17 citations · 20 across the 6 of their papers we have counts for
3 papers · 1 filter
Sparse Linear Mixed Model Selection via Streamlined Variational Bayes
Emanuele Degani, Luca Maestrini, Dorota Toczydłowska +1
Linear mixed models are a versatile statistical tool to study data by accounting for fixed effects and random effects from multiple sources of variability. In many situations, a la…
Fitting Structural Equation Models via Variational Approximations
Khue-Dung Dang, Luca Maestrini
Structural equation models are commonly used to capture the relationship between sets of observed and unobservable variables. Traditionally these models are fitted using frequentis…
A variational inference framework for inverse problems
Luca Maestrini, Robert G. Aykroyd, Matt P. Wand
A framework is presented for fitting inverse problem models via variational Bayes approximations. This methodology guarantees flexibility to statistical model specification for a b…