Improving road safety is hugely important with the number of deaths on t...
We consider the case of performing Bayesian inference for stochastic epi...
Improving road safety is hugely important with the number of deaths on t...
Stochastic kinetic models (SKMs) are increasingly used to account for th...
Proper function of a wastewater treatment plant (WWTP) relies on maintai...
The presence of protein aggregates in cells is a known feature of many h...
We consider the problem of inference for nonlinear, multivariate diffusi...
We present new methodologies for Bayesian inference on the rate paramete...
By exploiting mini-batch stochastic gradient optimisation, variational
i...
We perform fully Bayesian inference for stochastic differential equation...
Particle Markov chain Monte Carlo (pMCMC) is now a popular method for
pe...
We consider the task of generating draws from a Markov jump process (MJP...
We develop a spatio-temporal model to forecast sensor output at five
loc...
Performing fully Bayesian inference for the reaction rate constants gove...
Parameter inference for stochastic differential equations is challenging...