14 citations · 37 across the 19 of their papers we have counts for
Showing 2020 · stat.COShow all
2 papers · 2 filters
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…