论文标题
平行的进化多种尝试大都市马尔可夫链蒙特卡洛算法用于抽样空间分区
A Parallel Evolutionary Multiple-Try Metropolis Markov Chain Monte Carlo Algorithm for Sampling Spatial Partitions
论文作者
论文摘要
我们开发了一种进化马尔可夫链蒙特卡洛（EMCMC）算法，用于在大型且复杂的空间状态空间内采样空间分区。我们的算法将进化算法（EAS）的优势结合在一起，作为对状态空间遍历的优化启发式方法，以及马尔可夫链蒙特卡洛算法的理论收敛特性，用于从未知分布中取样。通过我们的优化启发式启发式通过定向搜索确定的本地最佳信息用于自适应地在多种尝试的大都会马尔可夫链模型的框架内以有希望的方向更新马尔可夫链，该模型结合了广义大都市狂暴的比率。我们通过集成一个并行的EA框架来实现大规模平行架构所提供的计算能力，从而进一步扩大了EMCMC算法的影响力，该算法可以通过并行运行的马尔可夫链。
We develop an Evolutionary Markov Chain Monte Carlo (EMCMC) algorithm for sampling spatial partitions that lie within a large and complex spatial state space. Our algorithm combines the advantages of evolutionary algorithms (EAs) as optimization heuristics for state space traversal and the theoretical convergence properties of Markov Chain Monte Carlo algorithms for sampling from unknown distributions. Local optimality information that is identified via a directed search by our optimization heuristic is used to adaptively update a Markov chain in a promising direction within the framework of a Multiple-Try Metropolis Markov Chain model that incorporates a generalized Metropolis-Hasting ratio. We further expand the reach of our EMCMC algorithm by harnessing the computational power afforded by massively parallel architecture through the integration of a parallel EA framework that guides Markov chains running in parallel.
