  Annealed importance sampling (AIS) is the gold standard for estimating
partition functions or marginal likelihoods, corresponding to importance
sampling over a path of distributions between a tractable base and an
unnormalized target. While AIS yields an unbiased estimator for any path,
existing literature has been primarily limited to the geometric mixture or
moment-averaged paths associated with the exponential family and KL divergence.
We explore AIS using $q$-paths, which include the geometric path as a special
case and are related to the homogeneous power mean, deformed exponential
family, and $\alpha$-divergence.
