87 citations · 196 across the 20 of their papers we have counts for
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Testing whether a Learning Procedure is Calibrated
Jon Cockayne, Matthew M. Graham, Chris J. Oates +2
A learning procedure takes as input a dataset and performs inference for the parameters of a model that is assumed to have given rise to the dataset. Here we consider learning…
Probabilistic Gradients for Fast Calibration of Differential Equation Models
Jon Cockayne, Andrew B. Duncan
Calibration of large-scale differential equation models to observational or experimental data is a widespread challenge throughout applied sciences and engineering. A crucial bottl…
BayesCG As An Uncertainty Aware Version of CG
Tim W. Reid, Ilse C. F. Ipsen, Jon Cockayne +1
The Bayesian Conjugate Gradient method (BayesCG) is a probabilistic generalization of the Conjugate Gradient method (CG) for solving linear systems with real symmetric positive def…
Optimal Thinning of MCMC Output
Marina Riabiz, Wilson Chen, Jon Cockayne +4
The use of heuristics to assess the convergence and compress the output of Markov chain Monte Carlo can be sub-optimal in terms of the empirical approximations that are produced. T…