29 citations · 51 across the 3 of their papers we have counts for
3 papers
stat.ML2019★ 22 cited
Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap
Edwin Fong, Simon Lyddon, Chris Holmes
Increasingly complex datasets pose a number of challenges for Bayesian inference. Conventional posterior sampling based on Markov chain Monte Carlo can be too computationally inten…
stat.ML2018★ 29 cited
Nonparametric learning from Bayesian models with randomized objective functions
S. P. Lyddon, S. G. Walker, C. C. Holmes
Bayesian learning is built on an assumption that the model space contains a true reflection of the data generating mechanism. This assumption is problematic, particularly in comple…
stat.ME2017
General Bayesian Updating and the Loss-Likelihood Bootstrap
Simon Lyddon, Chris Holmes, Stephen Walker
In this paper we revisit the weighted likelihood bootstrap, a method that generates samples from an approximate Bayesian posterior of a parametric model. We show that the same meth…