317 citations · 539 across the 26 of their papers we have counts for
28 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…
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…
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…
Scalable Bayesian computation for crossed and nested hierarchical models
Omiros Papaspiliopoulos, Timothée Stumpf-Fétizon, Giacomo Zanella
We develop sampling algorithms to fit Bayesian hierarchical models, the computational complexity of which scales linearly with the number of observations and the number of paramete…
Exact inference for a class of non-linear hidden Markov models on general state spaces
Guillaume Kon Kam King, Omiros Papaspiliopoulos, Matteo Ruggiero
Exact inference for hidden Markov models requires the evaluation of all distributions of interest - filtering, prediction, smoothing and likelihood - with a finite computational ef…