  Annealed importance sampling (AIS) is a common algorithm to estimate
partition functions of useful stochastic models. One important problem for
obtaining accurate AIS estimates is the selection of an annealing schedule.
Conventionally, an annealing schedule is often determined heuristically or is
simply set as a linearly increasing sequence. In this paper, we propose an
algorithm for the optimal schedule by deriving a functional that dominates the
AIS estimation error and by numerically minimizing this functional. We
experimentally demonstrate that the proposed algorithm mostly outperforms
conventional scheduling schemes with large quantization numbers.
