58 citations · 198 across the 19 of their papers we have counts for
3 papers · 1 filter
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…
Bayes Shrinkage at GWAS scale: Convergence and Approximation Theory of a Scalable MCMC Algorithm for the Horseshoe Prior
James E. Johndrow, Paulo Orenstein, Anirban Bhattacharya
The horseshoe prior is frequently employed in Bayesian analysis of high-dimensional models, and has been shown to achieve minimax optimal risk properties when the truth is sparse.…
Optimal approximating Markov chains for Bayesian inference
James E. Johndrow, Jonathan C. Mattingly, Sayan Mukherjee +1
The Markov Chain Monte Carlo method is the dominant paradigm for posterior computation in Bayesian analysis. It is common to control computation time by making approximations to th…