Pairwise Comparisons with Flexible Time-Dynamics
Pairwise Comparisons with Flexible Time-Dynamics
复制标题
具有灵活时间动力学的成对比较
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
M. Grossglauser
中科院分区：
文献类型：
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作者：
Lucas Maystre;Victor Kristof;M. Grossglauser
受运动员或团队相互竞争的技能随时间变化而变化的体育应用程序的启发，我们提出了一个可以捕获大范围时间动态的两两比较结果的概率模型。我们通过用连续时间高斯过程代替一类流行的两两比较模型的静态参数来实现这一点；这些过程的协方差函数使表达动态成为可能。我们开发了一个有效的推理算法，计算近似贝叶斯后验分布。尽管我们的模型很灵活，但我们的推理算法只需要对数据进行一些线性时间迭代，并且可以利用现代多处理器计算机体系结构。我们将我们的模型应用于几个体育结果的历史数据库，发现我们的方法在预测性能方面优于竞争对手的方法，扩展到数百万个观察结果，并生成引人注目的可视化，有助于理解和解释数据。
Inspired by applications in sports where the skill of players or teams competing against each other varies over time, we propose a probabilistic model of pairwise-comparison outcomes that can capture a wide range of time dynamics. We achieve this by replacing the static parameters of a class of popular pairwise-comparison models by continuous-time Gaussian processes; the covariance function of these processes enables expressive dynamics. We develop an efficient inference algorithm that computes an approximate Bayesian posterior distribution. Despite the flexbility of our model, our inference algorithm requires only a few linear-time iterations over the data and can take advantage of modern multiprocessor computer architectures. We apply our model to several historical databases of sports outcomes and find that our approach outperforms competing approaches in terms of predictive performance, scales to millions of observations, and generates compelling visualizations that help in understanding and interpreting the data.