Accelerating adaptation in the adaptive Metropolis-Hastings random walk algorithm
Accelerating adaptation in the adaptive Metropolis-Hastings random walk algorithm
复制标题
自适应 Metropolis-Hastings 随机游走算法中的加速适应
DOI：
10.1111/anzs.12344
复制
发表时间：
2021
影响因子：
1.1
通讯作者：
Spencer S
中科院分区：
文献类型：
--
作者：
Spencer S
Metropolis-Hastings随机游走算法仍然很受从业者的欢迎，因为它可以成功地应用于各种各样的情况，并且可以非常容易地实现。该算法的自适应版本使用来自马尔可夫链的早期迭代的信息来提高建议的效率。本文的目的是减少使建议适应目标所需的迭代次数，这在可能性评估耗时时尤为重要。首先，加速成形算法是自适应建议和自适应大都会算法的推广。它的目的是从目标的协方差矩阵的估计中去除来自链开始的误导信息。其次，加速缩放算法快速改变提案的规模，以达到目标接受率。这些方法的有用性说明了一系列的例子。
The Metropolis–Hastings random walk algorithm remains popular with practitioners due to the wide variety of situations in which it can be successfully applied and the extreme ease with which it can be implemented. Adaptive versions of the algorithm use information from the early iterations of the Markov chain to improve the efficiency of the proposal. The aim of this paper is to reduce the number of iterations needed to adapt the proposal to the target, which is particularly important when the likelihood is time‐consuming to evaluate. First, the accelerated shaping algorithm is a generalisation of both the adaptive proposal and adaptive Metropolis algorithms. It is designed to remove, from the estimate of the covariance matrix of the target, misleading information from the start of the chain. Second, the accelerated scaling algorithm rapidly changes the scale of the proposal to achieve a target acceptance rate. The usefulness of these approaches is illustrated with a range of examples.