Multiscale Modeling &Simulation, Volume 18, Issue 2, Page 646-673, January 2020.
We propose parameter optimization techniques for weighted ensemble sampling of Markov chains in the steady-state regime. Weighted ensemble consists of replicas of a Markov chain, each carrying a weight, that are periodically resampled according to their weights inside of each of a number of bins that partition state space. We derive, from first principles, strategies for optimizing the choices of weighted ensemble parameters, in particular the choice of bins and the number of replicas to maintain in each bin. In a simple numerical example, we compare our new strategies with more traditional ones and with direct Monte Carlo.

中文翻译：

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优化稳态加权集成采样

多尺度建模与仿真，第 18 卷，第 2 期，第 646-673 页，2020 年 1 月。
我们提出了在稳态状态下对马尔可夫链进行加权集成采样的参数优化技术。加权集成由马尔可夫链的副本组成，每个副本携带一个权重，根据它们在划分状态空间的多个 bin 中的每个 bin 内的权重定期重新采样。我们从第一原则推导出优化加权集成参数选择的策略，特别是箱的选择和每个箱中要维护的副本数量。在一个简单的数值示例中，我们将我们的新策略与更传统的策略和直接蒙特卡罗进行比较。