5 citations · 8 across the 2 of their papers we have counts for
4 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…
: A robust MCMC convergence diagnostic with uncertainty using decision tree classifiers
Ben Lambert, Aki Vehtari
Markov chain Monte Carlo (MCMC) has transformed Bayesian model inference over the past three decades: mainly because of this, Bayesian inference is now a workhorse of applied scien…
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…