362 citations · 635 across the 29 of their papers we have counts for
36 papers
Quasi-Newton Sequential Monte Carlo
Samuel Duffield, Sumeetpal S. Singh
Sequential Monte Carlo samplers represent a compelling approach to posterior inference in Bayesian models, due to being parallelisable and providing an unbiased estimate of the pos…
De-biasing particle filtering for a continuous time hidden Markov model with a Cox process observation model
Ruiyang Jin, Sumeetpal S. Singh, Nicolas Chopin
We develop a (nearly) unbiased particle filtering algorithm for a specific class of continuous-time state-space models, such that (a) the latent process is a linear Gaussian…
Bayesian Deep Learning with Multilevel Trace-class Neural Networks
Neil K. Chada, Ajay Jasra, Kody J. H. Law +1
In this article we consider Bayesian inference associated to deep neural networks (DNNs) and in particular, trace-class neural network (TNN) priors which can be preferable to tradi…
On resampling schemes for particle filters with weakly informative observations
Nicolas Chopin, Sumeetpal S. Singh, Tomás Soto +1
We consider particle filters with weakly informative observations (or `potentials') relative to the latent state dynamics. The particular focus of this work is on particle filters…
Ensemble Kalman Inversion for General Likelihoods
Samuel Duffield, Sumeetpal S. Singh
In this letter we generalise Ensemble Kalman inversion techniques to general Bayesian models where previously they were restricted to additive Gaussian likelihoods - all in the dif…
Gradient-Based Markov Chain Monte Carlo for Bayesian Inference With Non-Differentiable Priors
Jacob Vorstrup Goldman, Torben Sell, Sumeetpal Sidhu Singh
The use of non-differentiable priors in Bayesian statistics has become increasingly popular, in particular in Bayesian imaging analysis. Current state of the art methods are approx…