Markov random fields (MRFs) are difficult to evaluate as generative models
because computing the test log-probabilities requires the intractable partition
function. Annealed importance sampling (AIS) is widely used to estimate MRF
partition functions, and often yields quite accurate results. However, AIS is
prone to overestimate the log-likelihood with little indication that anything
is wrong. We present the Reverse AIS Estimator (RAISE), a stochastic lower
bound on the log-likelihood of an approximation to the original MRF model.
RAISE requires only the same MCMC transition operators as standard AIS.
Experimental results indicate that RAISE agrees closely with AIS
log-probability estimates for RBMs, DBMs, and DBNs, but typically errs on the
side of underestimating, rather than overestimating, the log-likelihood.