58 citations · 148 across the 25 of their papers we have counts for
3 papers · 1 filter
The Neural Moving Average Model for Scalable Variational Inference of State Space Models
Tom Ryder, Dennis Prangle, Andrew Golightly +1
Variational inference has had great success in scaling approximate Bayesian inference to big data by exploiting mini-batch training. To date, however, this strategy has been most a…
Efficient inference for stochastic differential equation mixed-effects models using correlated particle pseudo-marginal algorithms
Samuel Wiqvist, Andrew Golightly, Ashleigh T. McLean +1
Stochastic differential equation mixed-effects models (SDEMEMs) are flexible hierarchical models that are able to account for random variability inherent in the underlying time-dyn…
Ensemble MCMC: Accelerating Pseudo-Marginal MCMC for State Space Models using the Ensemble Kalman Filter
Christopher Drovandi, Richard G Everitt, Andrew Golightly +1
Particle Markov chain Monte Carlo (pMCMC) is now a popular method for performing Bayesian statistical inference on challenging state space models (SSMs) with unknown static paramet…