317 citations · 539 across the 26 of their papers we have counts for
10 papers · 1 filter
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…
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…
Dimension-Robust MCMC in Bayesian Inverse Problems
Victor Chen, Matthew M. Dunlop, Omiros Papaspiliopoulos +1
The methodology developed in this article is motivated by a wide range of prediction and uncertainty quantification problems that arise in Statistics, Machine Learning and Applied…
Exact sampling of diffusions with a discontinuity in the drift
Omiros Papaspiliopoulos, Gareth O. Roberts, Kasia B. Taylor
We introduce exact methods for the simulation of sample paths of one-dimensional diffusions with a discontinuity in the drift function. Our procedures require the simulation of fin…
Markov chain Monte Carlo for exact inference for diffusions
Giorgos Sermaidis, Omiros Papaspiliopoulos, Gareth O. Roberts +2
We develop exact Markov chain Monte Carlo methods for discretely-sampled, directly and indirectly observed diffusions. The qualification "exact" refers to the fact that the invaria…