论文标题
非可逆的马尔可夫连锁连锁蒙特卡洛，用于分区地图的抽样
Non-reversible Markov chain Monte Carlo for sampling of districting maps
论文作者
论文摘要
在统计框架中评估党派区域（Gerrymandering）的程度通常需要一系列区域计划，这些计划是从规定的概率分布中得出的，这些概率分布遵守现实和非党派标准。在本文中，我们介绍了新型的非可逆马尔可夫链蒙特 - 卡洛（MCMC）方法，用于采样此类区域计划，与先前使用的（可逆的）MCMC算法相比，该计划改善了混合特性。为此，我们通过考虑偏斜详细的平衡的概括来扩展当前在离散抽样空间上构建非可逆马尔可夫链的框架。我们提供了提出的算法的详细描述，并在数值实验中评估了它们的性能。
Evaluating the degree of partisan districting (Gerrymandering) in a statistical framework typically requires an ensemble of districting plans which are drawn from a prescribed probability distribution that adheres to a realistic and non-partisan criteria. In this article we introduce novel non-reversible Markov chain Monte-Carlo (MCMC) methods for the sampling of such districting plans which have improved mixing properties in comparison to previously used (reversible) MCMC algorithms. In doing so we extend the current framework for construction of non-reversible Markov chains on discrete sampling spaces by considering a generalization of skew detailed balance. We provide a detailed description of the proposed algorithms and evaluate their performance in numerical experiments.
