论文标题
纠正，反犯罪，多重重要性抽样
Tempered, Anti-trunctated, Multiple Importance Sampling
论文作者
论文摘要
重要性采样是一种蒙特卡洛方法，它引入了根据目标分布进行采样空间的建议分布。然而，对提案分布的校准对于达到效率至关重要，因此诉诸于自适应算法以调整此分布。在本文中，我们提出了一种新的副本重要性抽样方案，该方案称为钢化反截断的自适应多重重要性采样（TAMIS）算法。我们结合了回火方案和我们称为反截断的重量的新的非线性转化。为了提高效率，我们还担心不要增加目标密度的评估数量。结果，我们的建议是一种自动调整的顺序算法，对较差的初始建议稳健，不需要梯度计算，并且可以很好地缩放尺寸。
Importance sampling is a Monte Carlo method that introduces a proposal distribution to sample the space according to the target distribution. Yet calibration of the proposal distribution is essential to achieving efficiency, thus the resort to adaptive algorithms to tune this distribution. In the paper, we propose a new adpative importance sampling scheme, named Tempered Anti-truncated Adaptive Multiple Importance Sampling (TAMIS) algorithm. We combine a tempering scheme and a new nonlinear transformation of the weights we named anti-truncation. For efficiency, we were also concerned not to increase the number of evaluations of the target density. As a result, our proposal is an automatically tuned sequential algorithm that is robust to poor initial proposals, does not require gradient computations and scales well with the dimension.
