稳态加权集合抽样的优化
Optimizing weighted ensemble sampling of steady states
摘要：
我们提出了参数最优化技术的加权集成采样的马尔可夫链在稳态制度。加权系综由马尔可夫链的副本组成，每个副本携带一个权重，这些副本根据它们在划分状态空间的多个仓中的每个仓内的权重被周期性地重新采样。我们得出，从第一原则，策略优化加权合奏参数的选择，特别是箱的选择和副本的数量，以保持在每个箱。在一个简单的数值例子中，我们比较我们的新策略与更传统的，直接蒙特卡洛。
英文摘要：
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.
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