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    政大機構典藏 > 商學院 > 統計學系 > 期刊論文 >  Item 140.119/150736
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/150736


    Title: Rating players by Laplace’s approximation and dynamic modeling
    Authors: 翁久幸;華軒甫
    Weng, Ruby Chiu-Hsing;Hua, Hsuan-Fu;Chang, Ching-Ju;Lin, Tse-Ching
    Contributors: 統計系
    Keywords: Bradley–Terry;Elo;Laplace approximation;McNemar test;Posterior distribution
    Date: 2024-07
    Issue Date: 2024-04-11
    Abstract: The Elo rating system is a simple and widely used method for calculating players’ skills from paired comparison data. Many have extended it in various ways. Yet the question of updating players’ variances remains to be further explored. In this paper, we address the issue of variance update by using the Laplace approximation for posterior distributions, together with a random walk model for the dynamics of players’ strengths and a lower bound on player variance. The random walk model is motivated by the Glicko system, but here we assume nonidentically distributed increments to deal with player heterogeneity. Experiments on men’s professional matches showed that the prediction accuracy slightly improves when the variance update is performed. They also showed that new players’ strengths may be better captured with the variance update.
    Relation: International Journal of Forecasting, Vol.40, No.3, pp.1152-1165
    Data Type: article
    DOI 連結: https://doi.org/10.1016/j.ijforecast.2023.10.004
    DOI: 10.1016/j.ijforecast.2023.10.004
    Appears in Collections:[統計學系] 期刊論文

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