9 citations · 10 across the 6 of their papers we have counts for
6 papers
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…
Fast Physical Activity Suggestions: Efficient Hyperparameter Learning in Mobile Health
Marianne Menictas, Sabina Tomkins, Susan Murphy
Users can be supported to adopt healthy behaviors, such as regular physical activity, via relevant and timely suggestions on their mobile devices. Recently, reinforcement learning…
Streamlined Empirical Bayes Fitting of Linear Mixed Models in Mobile Health
Marianne Menictas, Sabina Tomkins, Susan A Murphy
To effect behavior change a successful algorithm must make high-quality decisions in real-time. For example, a mobile health (mHealth) application designed to increase physical act…
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…
Streamlined Computing for Variational Inference with Higher Level Random Effects
Tui H. Nolan, Marianne Menictas, Matt P. Wand
We derive and present explicit algorithms to facilitate streamlined computing for variational inference for models containing higher level random effects. Existing literature, such…