3 papers
stat.ME2025
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…
stat.CO2025
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…
stat.ME2024
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…