Uncertainty in Artificial Intelligence
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Firefly Monte Carlo: Exact MCMC with Subsets of Data
Dougal Maclaurin, Ryan Adams
Markov chain Monte Carlo (MCMC) is a popular and successful general-purpose tool for Bayesian inference. However, MCMC cannot be practi- cally applied to large data sets because of the prohibitive cost of evaluating every likelihood term at every iteration. Here we present Fire- fly Monte Carlo (FlyMC) an auxiliary variable MCMC algorithm that only queries the likeli- hoods of a potentially small subset of the data at each iteration yet simulates from the exact pos- terior distribution, in contrast to recent propos- als that are approximate even in the asymptotic limit. FlyMC is compatible with a wide variety of modern MCMC algorithms, and only requires a lower bound on the per-datum likelihood fac- tors. In experiments, we find that FlyMC gen- erates samples from the posterior more than an order of magnitude faster than regular MCMC, opening up MCMC methods to larger datasets than were previously considered feasible.
Pages: 543-552
PS Link:
PDF Link: /papers/14/p543-maclaurin.pdf
AUTHOR = "Dougal Maclaurin and Ryan Adams",
TITLE = "Firefly Monte Carlo: Exact MCMC with Subsets of Data",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "543--552"

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