17 citations · 20 across the 6 of their papers we have counts for
6 papers
Stochastic Variational Inference for GARCH Models
Hanwen Xuan, Luca Maestrini, Feng Chen +1
Stochastic variational inference algorithms are derived for fitting various heteroskedastic time series models. We examine Gaussian, t, and skew-t response GARCH models and fit the…
Second Term Improvement to Generalised Linear Mixed Model Asymptotics
Luca Maestrini, Aishwarya Bhaskaran, Matt P. Wand
A recent article on generalised linear mixed model asymptotics, Jiang et al. (2022), derived the rates of convergence for the asymptotic variances of maximum likelihood estimators.…
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…
The Inverse G-Wishart Distribution and Variational Message Passing
L. Maestrini, M. P. Wand
Message passing on a factor graph is a powerful paradigm for the coding of approximate inference algorithms for arbitrarily graphical large models. The notion of a factor graph fra…