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stat.CO2026
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…
stat.CO2024
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…
stat.CO2024
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…