257 citations · 405 across the 9 of their papers we have counts for
5 papers · 1 filter
Randomized Maximum Likelihood via High-Dimensional Bayesian Optimization
Valentin Breaz, Richard Wilkinson
Posterior sampling for high-dimensional Bayesian inverse problems is a common challenge in real-world applications. Randomized Maximum Likelihood (RML) is an optimization based met…
Considering discrepancy when calibrating a mechanistic electrophysiology model
Chon Lok Lei, Sanmitra Ghosh, Dominic G. Whittaker +14
Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ me…
ABC for climate: dealing with expensive simulators
Philip B. Holden, Neil R. Edwards, James Hensman +1
This paper is due to appear as a chapter of the forthcoming Handbook of Approximate Bayesian Computation (ABC) by S. Sisson, L. Fan, and M. Beaumont. We describe the challenge of c…
Accelerating ABC methods using Gaussian processes
Richard D Wilkinson
Approximate Bayesian computation (ABC) methods are used to approximate posterior distributions using simulation rather than likelihood calculations. We introduce Gaussian process (…
Approximate Bayesian computation (ABC) gives exact results under the assumption of model error
Richard D. Wilkinson
Approximate Bayesian computation (ABC) or likelihood-free inference algorithms are used to find approximations to posterior distributions without making explicit use of the likelih…