论文标题
阿尔卑斯式算法的偏度布朗运动和复杂性
Skew Brownian Motion and Complexity of the ALPS Algorithm
论文作者
论文摘要
模拟回火是允许MCMC算法在多模式目标密度π的模式之间移动的一种流行方法。该论文[24]引入了退火的Leap-Point采样器（ALP），以允许模式之间的快速运动。在本文中，我们证明，在适当的假设下，Alps算法的适当缩放版本薄弱地收敛到偏斜的布朗运动。我们的结果表明，在适当的假设下，Alps算法在时间O中混合（D [log（d）]^2）或O（d），具体取决于使用哪个版本。
Simulated tempering is a popular method of allowing MCMC algorithms to move between modes of a multimodal target density π. The paper [24] introduced the Annealed Leap-Point Sampler (ALPS) to allow for rapid movement between modes. In this paper, we prove that, under appropriate assumptions, a suitably scaled version of the ALPS algorithm converges weakly to skew Brownian motion. Our results show that under appropriate assumptions, the ALPS algorithm mixes in time O(d[log(d)]^2 ) or O(d), depending on which version is used.
