4 papers
Functional Estimation of the Marginal Likelihood
Omiros Papaspiliopoulos, Timothée Stumpf-Fétizon, Jonathan Weare
We propose a framework for computing, optimizing and integrating with respect to a smooth marginal likelihood in statistical models that involve high-dimensional parameters/latent…
Conjugate gradient methods for high-dimensional GLMMs
Andrea Pandolfi, Omiros Papaspiliopoulos, Giacomo Zanella
Generalized linear mixed models (GLMMs) are a widely used tool in statistical analysis. The main bottleneck of many computational approaches lies in the inversion of the high dimen…
Inference for multiple treatment effects using confounder importance learning
Omiros Papaspiliopoulos, David Rossell, Miquel Torrens-i-Dinarès
We address modelling and computational issues for multiple treatment effect inference under many potential confounders. Our main contribution is providing a trade-off between preve…
Partially factorized variational inference for high-dimensional mixed models
Max Goplerud, Omiros Papaspiliopoulos, Giacomo Zanella
While generalized linear mixed models are a fundamental tool in applied statistics, many specifications, such as those involving categorical factors with many levels or interaction…