论文标题
Q-Paths退火重要性抽样
Annealed Importance Sampling with q-Paths
论文作者
论文摘要
退火重要性采样（AIS）是估计分区函数或边际可能性的黄金标准，对应于在可障碍基础和非均衡目标之间的分布路径上进行的重要性采样。尽管AIS对于任何路径都产生了公正的估计量，但现有文献主要限于与指数族和KL差异相关的几何混合物或矩平的路径。我们使用$ Q $ - Paths探索AI，其中包括几何路径作为一种特殊情况，并且与均匀的功率平均值，变形指数家族和$α$ divergence有关。
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 $α$-divergence.
