28 citations · 86 across the 22 of their papers we have counts for
23 papers
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…
Prequential posteriors
Shreya Sinha-Roy, Richard G. Everitt, Christian P. Robert +1
Data assimilation is a fundamental task in updating forecasting models upon observing new data, with applications ranging from weather prediction to online reinforcement learning.…
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…
Improved MCMC with active subspaces
Leonardo Ripoli, Richard G. Everitt
Constantine et al. (2016) introduced a Metropolis-Hastings (MH) approach that target the active subspace of a posterior distribution: a linearly projected subspace that is informed…
Generalized Bayesian deep reinforcement learning
Shreya Sinha Roy, Richard G. Everitt, Christian P. Robert +1
Bayesian reinforcement learning (BRL) is a method that merges principles from Bayesian statistics and reinforcement learning to make optimal decisions in uncertain environments. As…
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…