Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom.
Proc Natl Acad Sci U S A. 2017 Jul 3;114(27):6924-6929. doi: 10.1073/pnas.1620497114. Epub 2017 Jun 20.
Conventional Monte Carlo simulations are stochastic in the sense that the acceptance of a trial move is decided by comparing a computed acceptance probability with a random number, uniformly distributed between 0 and 1. Here, we consider the case that the weight determining the acceptance probability itself is fluctuating. This situation is common in many numerical studies. We show that it is possible to construct a rigorous Monte Carlo algorithm that visits points in state space with a probability proportional to their average weight. The same approach may have applications for certain classes of high-throughput experiments and the analysis of noisy datasets.
传统的蒙特卡罗模拟在随机性方面的特点是，通过将计算得出的接受概率与在 0 到 1 之间均匀分布的随机数进行比较，来决定是否接受一个试探性移动。在这里，我们考虑权重本身波动的情况，这种情况在许多数值研究中很常见。我们表明，可以构建一个严格的蒙特卡罗算法，该算法以与它们的平均权重成比例的概率访问状态空间中的点。这种方法可能适用于某些高通量实验和噪声数据集的分析。
