82 citations · 204 across the 14 of their papers we have counts for
6 papers · 1 filter
Fast approximate Bayesian multidimensional scaling with consistency guarantees
Ami Sheth, Aaron Smith, Andrew J. Holbrook
Bayesian multidimensional scaling (BMDS) embeds objects in a low-dimensional space to approximately preserve an observed dissimilarity matrix. Compared to classic MDS, BMDS is…
No Free Lunch for Stochastic Gradient Langevin Dynamics
Natesh S. Pillai, Aaron Smith, Azeem Zaman
As sample sizes grow, scalability has become a central concern in the development of Markov chain Monte Carlo (MCMC) methods. One general approach to this problem, exemplified by t…
On Cyclical MCMC Sampling
Liwei Wang, Xinru Liu, Aaron Smith +1
Cyclical MCMC is a novel MCMC framework recently proposed by Zhang et al. (2019) to address the challenge posed by high-dimensional multimodal posterior distributions like those ar…
Importance is Important: Generalized Markov Chain Importance Sampling Methods
Guanxun Li, Aaron Smith, Quan Zhou
We show that for any multiple-try Metropolis algorithm, one can always accept the proposal and evaluate the importance weight that is needed to correct for the bias without extra c…
Parallel local approximation MCMC for expensive models
Patrick Conrad, Andrew Davis, Youssef Marzouk +2
Performing Bayesian inference via Markov chain Monte Carlo (MCMC) can be exceedingly expensive when posterior evaluations invoke the evaluation of a computationally expensive model…
The Use of a Single Pseudo-Sample in Approximate Bayesian Computation
Luke Bornn, Natesh Pillai, Aaron Smith +1
We analyze the computational efficiency of approximate Bayesian computation (ABC), which approximates a likelihood function by drawing pseudo-samples from the associated model. For…