136 citations · 255 across the 15 of their papers we have counts for
5 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…
Fast Approximate Inference for Arbitrarily Large Semiparametric Regression Models via Message Passing
M. P. Wand
We show how the notion of message passing can be used to streamline the algebra and computer coding for fast approximate inference in large Bayesian semiparametric regression model…
Variational inference for count response semiparametric regression
Jan Luts, Matt P. Wand
Fast variational approximate algorithms are developed for Bayesian semiparametric regression when the response variable is a count, i.e. a non-negative integer. We treat both the P…
Real-time semiparametric regression
Jan Luts, Tamara Broderick, Matt P. Wand
We develop algorithms for performing semiparametric regression analysis in real time, with data processed as it is collected and made immediately available via modern telecommunica…
On semiparametric regression with O'Sullivan penalised splines
M. P. Wand, J. T. Ormerod
This is an exposé on the use of O'Sullivan penalised splines in contemporary semiparametric regression, including mixed model and Bayesian formulations. O'Sullivan penalised spline…