117 citations · 121 across the 6 of their papers we have counts for
6 papers
Efficiently Computed Marginal Likelihoods for Multivariate Mixture Models
Martin Metodiev, Nicholas J. Irons, Marie Perrot-Dockès +2
We propose a new methodology for efficiently computing marginal likelihoods for univariate and multivariate mixture models. The estimator that this methodology proposes computes th…
US COVID-19 school closure was not cost-effective, but other measures were
Nicholas J. Irons, Adrian E. Raftery
Non-pharmaceutical interventions (NPIs) in response to the COVID-19 pandemic necessitated a trade-off between the health impacts of viral spread and the social and economic costs o…
Causally Sound Priors for Binary Experiments
Nicholas J. Irons, Carlos Cinelli
We introduce the BREASE framework for the Bayesian analysis of randomized controlled trials with a binary treatment and a binary outcome. Approaching the problem from a causal infe…
Easily Computed Marginal Likelihoods from Posterior Simulation Using the THAMES Estimator
Martin Metodiev, Marie Perrot-Dockès, Sarah Ouadah +2
We propose an easily computed estimator of marginal likelihoods from posterior simulation output, via reciprocal importance sampling, combining earlier proposals of DiCiccio et al…
Triangular Flows for Generative Modeling: Statistical Consistency, Smoothness Classes, and Fast Rates
Nicholas J. Irons, Meyer Scetbon, Soumik Pal +1
Triangular flows, also known as Knöthe-Rosenblatt measure couplings, comprise an important building block of normalizing flow models for generative modeling and density estimation,…
Estimating SARS-CoV-2 Infections from Deaths, Confirmed Cases, Tests, and Random Surveys
Nicholas J. Irons, Adrian E. Raftery
There are many sources of data giving information about the number of SARS-CoV-2 infections in the population, but all have major drawbacks, including biases and delayed reporting.…