5 citations · 8 across the 2 of their papers we have counts for
5 papers
Using flexible noise models to avoid noise model misspecification in inference of differential equation time series models
Richard Creswell, Ben Lambert, Chon Lok Lei +2
When modelling time series, it is common to decompose observed variation into a "signal" process, the process of interest, and "noise", representing nuisance factors that obfuscate…
Model Evidence with Fast Tree Based Quadrature
Thomas Foster, Chon Lok Lei, Martin Robinson +2
High dimensional integration is essential to many areas of science, ranging from particle physics to Bayesian inference. Approximating these integrals is hard, due in part to the d…
Probabilistic Inference on Noisy Time Series (PINTS)
Michael Clerx, Martin Robinson, Ben Lambert +4
Time series models are ubiquitous in science, arising in any situation where researchers seek to understand how a system's behaviour changes over time. A key problem in time series…
Separating the effects of experimental noise from inherent system variability in voltammetry: the Fe(CN) process
Martin Robinson, Alexandr N Simonov, Jie Zhang +2
Recently, we have introduced the use of techniques drawn from Bayesian statistics to recover kinetic and thermodynamic parameters from voltammetric data, and were able to show that…
Gaussian process emulation for discontinuous response surfaces with applications for cardiac electrophysiology models
Sanmitra Ghosh, David J. Gavaghan, Gary R. Mirams
Mathematical models of biological systems are beginning to be used for safety-critical applications, where large numbers of repeated model evaluations are required to perform uncer…