15 citations · 17 across the 3 of their papers we have counts for
3 papers
stat.CO2021★ 1 cited
The No-U-Turn Sampler as a Proposal Distribution in a Sequential Monte Carlo Sampler with a Near-Optimal L-Kernel
Lee Devlin, Paul Horridge, Peter L. Green +1
Markov Chain Monte Carlo (MCMC) is a powerful method for drawing samples from non-standard probability distributions and is utilized across many fields and disciplines. Methods suc…
stat.ME2020★ 1 cited
Ensemble Kalman filter based Sequential Monte Carlo Sampler for sequential Bayesian inference
Jiangqi Wu, Linjie Wen, Peter L Green +2
Many real-world problems require one to estimate parameters of interest, in a Bayesian framework, from data that are collected sequentially in time. Conventional methods for sampli…
stat.CO2020★ 15 cited
Increasing the efficiency of Sequential Monte Carlo samplers through the use of approximately optimal L-kernels
Peter L Green, Robert E Moore, Ryan J Jackson +2
By facilitating the generation of samples from arbitrary probability distributions, Markov Chain Monte Carlo (MCMC) is, arguably, \emph{the} tool for the evaluation of Bayesian inf…