5 citations · 20 across the 16 of their papers we have counts for
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stat.ME2022
Rapidly Mixing Multiple-try Metropolis Algorithms for Model Selection Problems
Hyunwoong Chang, Changwoo J. Lee, Zhao Tang Luo +2
The multiple-try Metropolis (MTM) algorithm is an extension of the Metropolis-Hastings (MH) algorithm by selecting the proposed state among multiple trials according to some weight…
stat.ME2022
Bayesian Multi-task Variable Selection with an Application to Differential DAG Analysis
Guanxun Li, Quan Zhou
In this paper, we study the Bayesian multi-task variable selection problem, where the goal is to select activated variables for multiple related data sets simultaneously. Our propo…
stat.ME2021★ 4 cited
Dimension-free Mixing for High-dimensional Bayesian Variable Selection
Quan Zhou, Jun Yang, Dootika Vats +2
Yang et al. (2016) proved that the symmetric random walk Metropolis--Hastings algorithm for Bayesian variable selection is rapidly mixing under mild high-dimensional assumptions. W…