82 citations · 204 across the 14 of their papers we have counts for
4 papers · 1 filter
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…
Sparse Bayesian multidimensional scaling(s)
Ami Sheth, Aaron Smith, Andrew J. Holbrook
Bayesian multidimensional scaling (BMDS) is a probabilistic dimension reduction tool that allows one to model and visualize data consisting of dissimilarities between pairs of obje…
Perturbations of Markov Chains
Daniel Rudolf, Aaron Smith, Matias Quiroz
This chapter surveys progress on three related topics in perturbations of Markov chains: the motivating question of when and how "perturbed" MCMC chains are developed, the theoreti…
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…