58 citations · 148 across the 25 of their papers we have counts for
5 papers · 1 filter
Black-Box Autoregressive Density Estimation for State-Space Models
Tom Ryder, Andrew Golighty, A. Stephen McGough +1
State-space models (SSMs) provide a flexible framework for modelling time-series data. Consequently, SSMs are ubiquitously applied in areas such as engineering, econometrics and ep…
Efficient sampling of conditioned Markov jump processes
Andrew Golightly, Chris Sherlock
We consider the task of generating draws from a Markov jump process (MJP) between two time-points at which the process is known. Resulting draws are typically termed bridges and th…
Sequential Bayesian inference for spatio-temporal models of temperature and humidity data
Yingying Lai, Andrew Golightly, Richard Boys
We develop a spatio-temporal model to forecast sensor output at five locations in North East England. The signal is described using coupled dynamic linear models, with spatial effe…
Correlated pseudo-marginal schemes for time-discretised stochastic kinetic models
Andrew Golightly, Emma Bradley, Tom Lowe +1
The challenging problem of conducting fully Bayesian inference for the reaction rate constants governing stochastic kinetic models (SKMs) is considered. Given the challenges underl…
Black-box Variational Inference for Stochastic Differential Equations
Thomas Ryder, Andrew Golightly, A. Stephen McGough +1
Parameter inference for stochastic differential equations is challenging due to the presence of a latent diffusion process. Working with an Euler-Maruyama discretisation for the di…