@inproceedings{fb50b2156f524b3090b611d57f9cdec5,

title = "Accelerating MCMC via parallel predictive prefetching",

abstract = "Parallel predictive prefetching is a new framework for accelerating a large class of widelyused Markov chain Monte Carlo (MCMC) algorithms. It speculatively evaluates many potential steps of an MCMC chain in parallel while exploiting fast, iterative approximations to the target density. This can accelerate sampling from target distributions in Bayesian inference problems. Our approach takes advantage of whatever parallel resources are available, but produces results exactly equivalent to standard serial execution. In the initial burn-in phase of chain evaluation, we achieve speedup close to linear in the number of available cores.",

author = "Elaine Angelino and Eddie Kohler and Amos Waterland and Margo Seltzer and Adams, {Ryan P.}",

year = "2014",

language = "English (US)",

series = "Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014",

publisher = "AUAI Press",

pages = "22--31",

editor = "Zhang, {Nevin L.} and Jin Tian",

booktitle = "Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014",

note = "30th Conference on Uncertainty in Artificial Intelligence, UAI 2014 ; Conference date: 23-07-2014 Through 27-07-2014",

}