一种迭代马尔可夫评级方法

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2020-10-05 DOI:10.1515/jqas-2019-0070

Stephen Devlin, T. Treloar, Molly Creagar, S. Cassels

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引用次数: 0

摘要

摘要本文介绍了一种简单自然的马尔可夫评级方法。我们证明了这种迭代马尔可夫方法收敛于通常的全局马尔可夫评级，并且与众所周知的Elo评级有着密切的关系。结合最近关于全局马尔可夫方法与布拉德利-特里(BT)模型中评级向量的最大似然估计之间关系的结果，我们连接并探索了真实和模拟数据上的全局和迭代马尔可夫，Elo和布拉德利-特里评级。

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An iterative Markov rating method

Abstract We introduce a simple and natural iterative version of the well-known and widely studied Markov rating method. We show that this iterative Markov method converges to the usual global Markov rating, and shares a close relationship with the well-known Elo rating. Together with recent results on the relationship between the global Markov method and the maximum likelihood estimate of the rating vector in the Bradley–Terry (BT) model, we connect and explore the global and iterative Markov, Elo, and Bradley–Terry ratings on real and simulated data.

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.