Response to Cho and Liu, “Sampling from complicated and unknown distributions: Monte Carlo and Markov chain Monte Carlo methods for redistricting”
Response to Cho and Liu, “Sampling from complicated and unknown distributions: Monte Carlo and Markov chain Monte Carlo methods for redistricting”
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对 Cho 和 Liu 的回应，“从复杂和未知的分布中采样：用于重新划分的蒙特卡罗和马尔可夫链蒙特卡罗方法”
DOI：
10.1016/j.physa.2018.10.057
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Samuel S.
中科院分区：
文献类型：
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作者：
William T. Adler;Samuel S.
一个具有法律意义的问题是，制定的政治选区地图是否具有“代表性”。为了回答这个问题，最近的工作使用了马尔可夫链蒙特卡罗（MCMC）方法来产生映射的零分布。Cho和Liu最近的一篇文章批评了Fifield等人使用MCMC重新划分选区的一种特殊实现。本评论的目的是提请注意赵和刘遗漏的两个事实，如果包括在内，将严重削弱他们的结论。Cho和Liu特别指出，Fifield等人的算法无法近似已知的目标分布，但忽略了Fifield等人使用并行和模拟回火，这大大提高了近似性。其次，Cho和Liu认为，当马尔可夫链混合时，检测过于困难；他们忽略了Fifield等人为此目的而使用的诊断方法。
A question of legal significance is whether an enacted map of political districts is “typical.” Recent work has used Markov chain Monte Carlo (MCMC) methods to produce null distributions of maps in order to answer this question. A recent article by Cho and Liu critiques one particular implementation of MCMC for redistricting, that of Fifield et al. The goal of the present commentary is to draw attention to two facts omitted by Cho and Liu that, if included, would have severely weakened their conclusions. In particular, Cho and Liu point out that Fifield et al.’s algorithm fails to approximate a known target distribution, but neglect Fifield et al.’s use of parallel and simulated tempering, which greatly improves the approximation. Secondly, Cho and Liu argue that it is overly difficult to detect when Markov chains have mixed; they neglect to mention diagnostics used for this exact purpose in Fifield et al.