  We introduce an extension to annealed importance sampling that uses
Hamiltonian dynamics to rapidly estimate normalization constants. We
demonstrate this method by computing log likelihoods in directed and undirected
probabilistic image models. We compare the performance of linear generative
models with both Gaussian and Laplace priors, product of experts models with
Laplace and Student's t experts, the mc-RBM, and a bilinear generative model.
We provide code to compare additional models.
