136 citations · 255 across the 15 of their papers we have counts for
16 papers
Structure Learning on Clustered Data
Ryan Thompson, Matt P. Wand, Veerabhadran Baladandayuthapani
Recent algorithmic advances have made directed acyclic graph (DAG) structure learning scalable for causal discovery. Yet, the currently available techniques assume a completely hom…
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.…
Dispersion Parameter Extension of Precise Generalized Linear Mixed Model Asymptotics
Aishwarya Bhaskaran, Matt P. Wand
We extend a recently established asymptotic normality theorem for generalized linear mixed models to include the dispersion parameter. The new results show that the maximum likelih…
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…
Density Estimation via Bayesian Inference Engines
M. P. Wand, J. C. F. Yu
We explain how effective automatic probability density function estimates can be constructed using contemporary Bayesian inference engines such as those based on no-U-turn sampling…
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…