14 citations · 37 across the 19 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.CO2020★ 5 cited
No Free Lunch for Approximate MCMC
James E. Johndrow, Natesh S. Pillai, Aaron Smith
It is widely known that the performance of Markov chain Monte Carlo (MCMC) can degrade quickly when targeting computationally expensive posterior distributions, such as when the sa…
stat.CO2020★ 3 cited
Rate-optimal refinement strategies for local approximation MCMC
Andrew D. Davis, Youssef Marzouk, Aaron Smith +1
Many Bayesian inference problems involve target distributions whose density functions are computationally expensive to evaluate. Replacing the target density with a local approxima…