9 citations · 23 across the 9 of their papers we have counts for
10 papers · 1 filter
Exploring Pareto smoothing in sequential Monte Carlo
Jia Le Tan, Nicola D. Walker, Richard G. Everitt
A popular technique for reducing the variance of importance sampling (IS) estimators is to modify the weights of some importance points. One approach is to truncate the largest wei…
Inference for Diffusion Processes via Controlled Sequential Monte Carlo and Splitting Schemes
Shu Huang, Richard G. Everitt, Massimiliano Tamborrino +1
We introduce an inferential framework for a wide class of semi-linear stochastic differential equations (SDEs). Recent work has shown that numerical splitting schemes can preserve…
Sequential Monte Carlo with active subspaces
Leonardo Ripoli, Richard G. Everitt
Monte Carlo methods, such as Markov chain Monte Carlo (MCMC), remain the most regularly-used approach for implementing Bayesian inference. However, the computational cost of these…
Rare event ABC-SMC
Ivis Kerama, Thomas Thorne, Richard G. Everitt
Approximate Bayesian computation (ABC) is a well-established family of Monte Carlo methods for performing approximate Bayesian inference in the case where an ``implicit'' model is…
Revisiting the balance heuristic for estimating normalising constants
Felipe J Medina-Aguayo, Richard G Everitt
Multiple importance sampling estimators are widely used for computing intractable constants due to its reliability and robustness. The celebrated balance heuristic estimator belong…
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…