362 citations · 635 across the 29 of their papers we have counts for
9 papers · 1 filter
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…
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…
Trace-class Gaussian priors for Bayesian learning of neural networks with MCMC
Torben Sell, Sumeetpal S. Singh
This paper introduces a new neural network based prior for real valued functions on which, by construction, is more easily and cheaply scaled up in the domain dimensi…
Online Particle Smoothing with Application to Map-matching
Samuel Duffield, Sumeetpal S. Singh
We introduce a novel method for online smoothing in state-space models that utilises a fixed-lag approximation to overcome the well known issue of path degeneracy. Unlike classical…
On Large Lag Smoothing for Hidden Markov Models
Jeremie Houssineau, Ajay Jasra, Sumeetpal S. Singh
In this article we consider the smoothing problem for hidden Markov models (HMM). Given a hidden Markov chain and observations , our objectiv…