Open AccessImproving Operational Intensity in Data Bound Markov Chain Monte Carlo
Author(s) -
Balázs Németh,
Tom Haber,
Thomas J. Ashby,
Wim Lamotte
Publication year - 2017
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2017.05.024
Subject(s) - computer science , markov chain monte carlo , leverage (statistics) , markov chain , cache , monte carlo method , bayesian probability , computation , parallel computing , algorithm , machine learning , artificial intelligence , statistics , mathematics
Part of the work presented in this paper was funded by Johnson & Johnson. This project has received funding from the European Union’s Horizon 2020 Research and Innovation programme under Grant Agreement no. 671555.
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