107 citations · 162 across the 14 of their papers we have counts for
15 papers
Sequential Bayesian inference for stochastic epidemic models of cumulative incidence
Sam A. Whitaker, Andrew Golightly, Colin S. Gillespie +1
Epidemics are inherently stochastic, and stochastic models provide an appropriate way to describe and analyse such phenomena. Given temporal incidence data consisting of, for examp…
MATILDA.FT, a Mesoscale Simulation Package for Inhomogeneous Soft Matter
Zuzanna M. Jedlinska, Christian Tabedzki, Colin Gillespie +3
In this paper we announce the public release of a massively-parallel, GPU-accelerated software, which is the first to combine both coarse-grained molecular dynamics and field-theor…
Parameter inference for a stochastic kinetic model of expanded polyglutamine proteins
Holly F. Fisher, Richard J. Boys, Colin S. Gillespie +2
The presence of protein aggregates in cells is a known feature of many human age-related diseases, such as Huntington's disease. Simulations using fixed parameter values in a model…
Bayesian experimental design without posterior calculations: an adversarial approach
Dennis Prangle, Sophie Harbisher, Colin S Gillespie
Most computational approaches to Bayesian experimental design require making posterior calculations repeatedly for a large number of potential designs and/or simulated datasets. Th…
Bayesian inference for a partially observed birth-death process using data on proportions
Richard J. Boys, Holly F. Ainsworth, Colin S. Gillespie
Stochastic kinetic models are often used to describe complex biological processes. Typically these models are analytically intractable and have unknown parameters which need to be…
Efficient construction of Bayes optimal designs for stochastic process models
Colin S. Gillespie, Richard J. Boys
Stochastic process models are now commonly used to analyse complex biological, ecological and industrial systems. Increasingly there is a need to deliver accurate estimates of mode…