  We introduce a new embarrassingly parallel parameter learning algorithm for
Markov random fields with untied parameters which is efficient for a large
class of practical models. Our algorithm parallelizes naturally over cliques
and, for graphs of bounded degree, its complexity is linear in the number of
cliques. Unlike its competitors, our algorithm is fully parallel and for
log-linear models it is also data efficient, requiring only the local
sufficient statistics of the data to estimate parameters.
