102 citations · 277 across the 26 of their papers we have counts for
6 papers · 1 filter
Bayesian score calibration for approximate models
Joshua J Bon, David J Warne, David J Nott +1
Scientists continue to develop increasingly complex mechanistic models to reflect their knowledge more realistically. Statistical inference using these models can be challenging si…
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…
Rapid Bayesian inference for expensive stochastic models
David J. Warne, Ruth E. Baker, Matthew J. Simpson
Almost all fields of science rely upon statistical inference to estimate unknown parameters in theoretical and computational models. While the performance of modern computer hardwa…
Vector operations for accelerating expensive Bayesian computations -- a tutorial guide
David J. Warne, Scott A. Sisson, Christopher Drovandi
Many applications in Bayesian statistics are extremely computationally intensive. However, they are often inherently parallel, making them prime targets for modern massively parall…
Multilevel rejection sampling for approximate Bayesian computation
David J. Warne, Ruth E. Baker, Matthew J. Simpson
Likelihood-free methods, such as approximate Bayesian computation, are powerful tools for practical inference problems with intractable likelihood functions. Markov chain Monte Car…