activity
20182022
most citedMultifidelity Approximate Bayesian Computation

18 citations · 38 across the 5 of their papers we have counts for

collaborators

5 papers

stat.AP2022★ 1 cited

Assessments and developments in constructing a National Health Index for policy making, in the United Kingdom

Anna Freni-Sterrantino, Thomas P Prescott, Greg Ceely +2

Composite indicators are a useful tool to summarize, measure and compare changes among different communities. The UK Office for National Statistics has created an annual England He…

stat.CO2021

Efficient Multifidelity Likelihood-Free Bayesian Inference with Adaptive Computational Resource Allocation

Thomas P Prescott, David J Warne, Ruth E Baker

Likelihood-free Bayesian inference algorithms are popular methods for calibrating the parameters of complex, stochastic models, required when the likelihood of the observed data is…

stat.CO2021★ 14 cited

Multifidelity multilevel Monte Carlo to accelerate approximate Bayesian parameter inference for partially observed stochastic processes

David J. Warne, Thomas P. Prescott, Ruth E. Baker +1

Models of stochastic processes are widely used in almost all fields of science. Theory validation, parameter estimation, and prediction all require model calibration and statistica…

stat.CO2020★ 5 cited

Multifidelity Approximate Bayesian Computation with Sequential Monte Carlo Parameter Sampling

Thomas P. Prescott, Ruth E. Baker

Multifidelity approximate Bayesian computation (MF-ABC) is a likelihood-free technique for parameter inference that exploits model approximations to significantly increase the spee…

stat.CO2018★ 18 cited

Multifidelity Approximate Bayesian Computation

Thomas P Prescott, Ruth E Baker

A vital stage in the mathematical modelling of real-world systems is to calibrate a model's parameters to observed data. Likelihood-free parameter inference methods, such as Approx…