Jul 24, 2023
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Annealed Importance Sampling (AIS) synthesizes weighted samples from an intractable distribution given its unnormalized density function. This algorithm relies on a sequence of interpolating distributions bridging the target to an initial tractable distribution such as the well-known geometric mean path of unnormalized distributions which is assumed to be suboptimal in general. In this paper, we prove that the geometric annealing corresponds to the optimal adaptive distribution path with respect to the KL-divergence between the current particle distribution and the desired target and we derive the constant rate discretization schedule for this annealing sequence, which adjusts the schedule to difficulty of moving samples between initial and the target distributions. We further extend our results to f-divergences and present the respective dynamics of annealing sequences. We empirically show that constant rate AIS performs well on multiple benchmark distributions while avoiding the computationally expensive tuning loop in existing Adaptive AIS.Annealed Importance Sampling (AIS) synthesizes weighted samples from an intractable distribution given its unnormalized density function. This algorithm relies on a sequence of interpolating distributions bridging the target to an initial tractable distribution such as the well-known geometric mean path of unnormalized distributions which is assumed to be suboptimal in general. In this paper, we prove that the geometric annealing corresponds to the optimal adaptive distribution path with respect…
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