18 citations · 38 across the 5 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
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…