  Within the next few years, Advanced LIGO and Virgo should detect
gravitational waves from binary neutron star and neutron star-black hole
mergers. These sources are also predicted to power a broad array of
electromagnetic transients. Because the electromagnetic signatures can be faint
and fade rapidly, observing them hinges on rapidly inferring the sky location
from the gravitational-wave observations. Markov chain Monte Carlo methods for
gravitational-wave parameter estimation can take hours or more. We introduce
BAYESTAR, a rapid, Bayesian, non-Markov chain Monte Carlo sky localization
algorithm that takes just seconds to produce probability sky maps that are
comparable in accuracy to the full analysis. Prompt localizations from BAYESTAR
will make it possible to search electromagnetic counterparts of compact binary
mergers.
