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.

翻译：重要程度取样是一种蒙特卡洛法,它引入了一种根据目标分布对空间进行抽样的建议分配方法。然而,对建议分配进行校准对于提高效率至关重要,因此使用适应性算法来调和这一分布。在论文中,我们提出了一个新的具有额外重要性的抽样方案,名为TAMIS(TAMIS),我们结合了一种调制办法和一种非线性的新变换,我们称之为反排泄的重量。关于效率,我们还关心的是不增加目标密度的评价数量。因此,我们的提案是一种自动调整的顺序算法,它对于落后的初步提议是强有力的,不需要梯度计算和尺度与尺寸相适应。