344 citations · 885 across the 35 of their papers we have counts for
5 papers · 1 filter
Fast Approximate Bayesian Contextual Cold Start Learning (FAB-COST)
Jack R. McKenzie, Peter A. Appleby, Thomas House +1
Cold-start is a notoriously difficult problem which can occur in recommendation systems, and arises when there is insufficient information to draw inferences for users or items. To…
Key Questions for Modelling COVID-19 Exit Strategies
Robin N Thompson, T Deirdre Hollingsworth, Valerie Isham +40
Combinations of intense non-pharmaceutical interventions ('lockdowns') were introduced in countries worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to impl…
Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example
Christopher E. Overton, Helena B. Stage, Shazaad Ahmad +16
During an infectious disease outbreak, biases in the data and complexities of the underlying dynamics pose significant challenges in mathematically modelling the outbreak and desig…
Challenges in control of Covid-19: short doubling time and long delay to effect of interventions
Lorenzo Pellis, Francesca Scarabel, Helena B. Stage +14
Early assessments of the spreading rate of COVID-19 were subject to significant uncertainty, as expected with limited data and difficulties in case ascertainment, but more reliable…
NuZZ: numerical Zig-Zag sampling for general models
Filippo Pagani, Augustin Chevallier, Sam Power +2
Markov chain Monte Carlo (MCMC) is a key algorithm in computational statistics, and as datasets grow larger and models grow more complex, many popular MCMC algorithms become too co…