activity
20182022
most citedCOVID-19 and the difficulty of inferring epidemiological parameters from clinical data

14 citations · 29 across the 3 of their papers we have counts for

collaborators

5 papers

stat.ML20229 cited

Robust Neural Posterior Estimation and Statistical Model Criticism

Daniel Ward, Patrick Cannon, Mark Beaumont +2

Computer simulations have proven a valuable tool for understanding complex phenomena across the sciences. However, the utility of simulators for modelling and forecasting purposes…

stat.ME20206 cited

qgam: Bayesian non-parametric quantile regression modelling in R

Matteo Fasiolo, Simon N. Wood, Margaux Zaffran +2

Generalized additive models (GAMs) are flexible non-linear regression models, which can be fitted efficiently using the approximate Bayesian methods provided by the mgcv R package.…

stat.AP2020

Additive stacking for disaggregate electricity demand forecasting

Christian Capezza, Biagio Palumbo, Yannig Goude +2

Future grid management systems will coordinate distributed production and storage resources to manage, in a cost effective fashion, the increased load and variability brought by th…

q-bio.QM202014 cited

COVID-19 and the difficulty of inferring epidemiological parameters from clinical data

Simon N. Wood, Ernst C. Wit, Matteo Fasiolo +1

Knowing the infection fatality ratio (IFR) is of crucial importance for evidence-based epidemic management: for immediate planning; for balancing the life years saved against the l…

stat.ME2018

Scalable visualisation methods for modern Generalized Additive Models

Matteo Fasiolo, Raphaël Nedellec, Yannig Goude +1

In the last two decades the growth of computational resources has made it possible to handle Generalized Additive Models (GAMs) that formerly were too costly for serious applicatio…