13 citations · 13 across the 2 of their papers we have counts for
3 papers
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…
Spatiotemporal blocking of the bouncy particle sampler for efficient inference in state space models
Jacob Vorstrup Goldman, Sumeetpal Sidhu Singh
We propose a novel blocked version of the continuous-time bouncy particle sampler of [Bouchard-Côté et al., 2018] which is applicable to any differentiable probability density. Thi…
Accelerated Sampling on Discrete Spaces with Non-Reversible Markov Processes
Samuel Power, Jacob Vorstrup Goldman
We consider the task of MCMC sampling from a distribution defined on a discrete space. Building on recent insights provided in [Zan19], we devise a class of efficient continuous-ti…