Abstract Irreversible and rejection-free Monte Carlo methods, recently developed in physics under the name event-chain and known in statistics as piecewise deterministic Monte Carlo (PDMC), have proven to produce clear acceleration over standard Monte Carlo methods, thanks to the reduction of their random-walk behavior. However, while applying such schemes to standard statistical models, one generally needs to introduce an additional randomization for sake of correctness. We propose here a new class of event-chain Monte Carlo methods that reduces this extra-randomization to a bare minimum. We compare the efficiency of this new methodology to standard PDMC and Monte Carlo methods. Accelerations up to several magnitudes and reduced dimensional scalings are exhibited. Supplementary materials for this article are available online.

中文翻译：

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前向事件链蒙特卡罗：在不可逆马尔可夫链中通过随机性控制进行快速采样

摘要 不可逆和无拒绝的蒙特卡罗方法，最近在物理学中以事件链的名义发展起来，在统计学中被称为分段确定性蒙特卡罗 (PDMC)，已被证明比标准蒙特卡罗方法产生明显的加速，这要归功于减少了他们的随机游走行为。然而，在将此类方案应用于标准统计模型时，为了正确性，通常需要引入额外的随机化。我们在这里提出了一类新的事件链蒙特卡罗方法，将这种额外的随机化减少到最低限度。我们将这种新方法与标准 PDMC 和蒙特卡罗方法的效率进行了比较。表现出高达几个数量级的加速度和减少的尺寸缩放。本文的补充材料可在线获取。