在线技能评级建模与推理的状态空间视角
A State-Space Perspective on Modelling and Inference for Online Skill Rating
AI总结：
该研究从状态空间模型视角出发，总结竞技体育技能评级现有方法，提出基于序贯蒙特卡洛和离散隐马尔可夫模型的新方案，梳理建模与推理步骤，探讨大规模扩展的效率优化方法，并通过开源Python包提供可复现的实验对比。
摘要：
我们总结了竞技体育中常用的技能评级方法及其推理范式，并提出了基于序贯蒙特卡洛和离散隐马尔可夫模型的新方法。我们倡导采用状态空间模型视角，将选手技能表征为时变变量，比赛结果作为观测量。我们探究了模型构建步骤以及滤波、平滑、参数估计这三个推理阶段。我们检验了扩展到大量选手和比赛时面临的挑战，重点介绍了可提升统计与计算效率的主要近似方法和降维手段。我们还在一个真实实验流程中对比了各类方法，该流程可通过我们的开源Python包https://github.com/SamDuffield/abile轻松复现与扩展。
英文摘要：
We summarise popular methods used for skill rating in competitive sports, along with their inferential paradigms and introduce new approaches based on sequential Monte Carlo and discrete hidden Markov models. We advocate for a state-space model perspective, wherein players' skills are represented as time-varying, and match results serve as observed quantities. We explore the steps to construct the model and the three stages of inference: filtering, smoothing and parameter estimation. We examine the challenges of scaling up to numerous players and matches, highlighting the main approximations and reductions which facilitate statistical and computational efficiency. We additionally compare approaches in a realistic experimental pipeline that can be easily reproduced and extended with our open-source Python package, https://github.com/SamDuffield/abile.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
