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A0177
Title: Optimal tuning of subsampling Hamiltonian Monte Carlo Authors:  Mattias Villani - Stockholm University (Sweden) [presenting]
Robert Kohn - University of New South Wales (Australia)
Minh-Ngoc Tran - University of Sydney (Australia)
Matias Quiroz - University of Technology Sydney (Australia)
Doan Khue Dung Dang - University of New South Wales (Australia)
Abstract: Hamiltonian Monte Carlo (HMC) is an increasingly popular simulation algorithm for Bayesian inference which has proven to be especially suitable in high-dimensional problems. A drawback of HMC is that it requires a large number of evaluations of the posterior gradient, which can be computationally costly, particularly in problems with large datasets. Results on accelerating HMC by data subsampling and how to optimally tune the algorithm are presented.