19 citations · 21 across the 11 of their papers we have counts for
8 papers · 1 filter
Orthogonalized Design Matrices Speed-ups of Bayesian Semiparametric Regression
Nurul Fitriyani, Matt P. Wand
We explain how important classes of Bayesian semiparametric regression fitting and inference procedures can be sped up, significantly, via the use of orthogonalized design matrices…
Online Semiparametric Regression via Sequential Monte Carlo
Marianne Menictas, Chris J. Oates, Matt P. Wand
We develop and describe online algorithms for performing online semiparametric regression analyses. Earlier work on this topic is in Luts, Broderick & Wand (J. Comput. Graph. Stati…
Bayesian Generalized Additive Model Selection Including a Fast Variational Option
Virginia X. He, Matt P. Wand
We use Bayesian model selection paradigms, such as group least absolute shrinkage and selection operator priors, to facilitate generalized additive model selection. Our approach al…
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…
Streamlined Variational Inference for Linear Mixed Models with Crossed Random Effects
Marianne Menictas, Gioia Di Credico, Matt P. Wand
We derive streamlined mean field variational Bayes algorithms for fitting linear mixed models with crossed random effects. In the most general situation, where the dimensions of th…
Streamlined Variational Inference for Higher Level Group-Specific Curve Models
M. Menictas, T. H. Nolan, D. G. Simpson +1
A two-level group-specific curve model is such that the mean response of each member of a group is a separate smooth function of a predictor of interest. The three-level extension…