116 citations · 305 across the 40 of their papers we have counts for
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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…
stat.CO2020★ 6 cited
An approximate KLD based experimental design for models with intractable likelihoods
Ziqiao Ao, Jinglai Li
Data collection is a critical step in statistical inference and data science, and the goal of statistical experimental design (ED) is to find the data collection setup that can pro…