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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…