2 citations · 4 across the 3 of their papers we have counts for
4 papers
Tensor-variate Gaussian process regression for efficient emulation of complex systems: comparing regressor and covariance structures in outer product and parallel partial emulators
Daria Semochkina, Samuel E. Jackson, David C. Woods
Multi-output Gaussian process regression has become an important tool in uncertainty quantification, for building emulators of computationally expensive simulators, and other areas…
Incorporating additional evidence as prior information to resolve non-identifiability in Bayesian disease model calibration. A tutorial
Daria Semochkina, Cathal Walsh
Disease models are used to examine the likely impact of therapies, interventions and public policy changes. Ensuring that these are well calibrated on the basis of available data a…
Multi-objective optimisation using expected quantile improvement for decision making in disease outbreaks
Daria Semochkina, Alexander I. J. Forrester, David C Woods
Optimization under uncertainty is important in many applications, particularly to inform policy and decision making in areas such as public health. A key source of uncertainty aris…
Uncertainty quantification for epidemiological forecasts of COVID-19 through combinations of model predictions
D. S. Silk, V. E. Bowman, D. Semochkina +2
Scientific advice to the UK government throughout the COVID-19 pandemic has been informed by ensembles of epidemiological models provided by members of the Scientific Pandemic Infl…