5 citations · 20 across the 16 of their papers we have counts for
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stat.CO2021★ 1 cited
Rapid Convergence of Informed Importance Tempering
Quan Zhou, Aaron Smith
Informed Markov chain Monte Carlo (MCMC) methods have been proposed as scalable solutions to Bayesian posterior computation on high-dimensional discrete state spaces, but theoretic…
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…
math.ST2021★ 4 cited
Complexity analysis of Bayesian learning of high-dimensional DAG models and their equivalence classes
Quan Zhou, Hyunwoong Chang
Structure learning via MCMC sampling is known to be very challenging because of the enormous search space and the existence of Markov equivalent DAGs. Theoretical results on the mi…