78 citations · 90 across the 5 of their papers we have counts for
6 papers
Understanding the impact of numerical solvers on inference for differential equation models
Richard Creswell, Katherine M. Shepherd, Ben Lambert +5
Most ordinary differential equation (ODE) models used to describe biological or physical systems must be solved approximately using numerical methods. Perniciously, even those solv…
Autocorrelated measurement processes and inference for ordinary differential equation models of biological systems
Ben Lambert, Chon Lok Lei, Martin Robinson +5
Ordinary differential equation models are used to describe dynamic processes across biology. To perform likelihood-based parameter inference on these models, it is necessary to spe…
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…