量化人类极限运动表现的界限：对精英十项全能运动员的贝叶斯分析
Quantifying the limits of human athletic performance: A Bayesian analysis of elite decathletes
AI总结：
本文通过贝叶斯模型分析十项全能运动员表现，旨在量化人类极限运动能力的边界。
摘要：
由于十项全能测试了多种运动能力，包括短跑、投掷、跳跃和耐力，许多人认为它是对运动能力的终极考验。基于这种观点，估计最大十项全能得分并理解达到该得分所需条件，能提供关于人类运动潜力上限的见解。为此，我们开发了一个贝叶斯组合模型，用于预测个体运动员在十项全能的10个事件中的表现。除了捕捉性能的非线性时间趋势外，我们的模型还仔细捕捉了事件表现与所有先前事件之间的依赖性。使用我们的模型，我们可以模拟并评估最大可能得分的分布，并识别出可能接近此极限的运动员配置。
英文摘要：
Because the decathlon tests many facets of athleticism, including sprinting, throwing, jumping, and endurance, many consider it to be the ultimate test of athletic ability. On this view, estimating the maximal decathlon score and understanding what it would take to achieve that score provides insight into the upper limits of human athletic potential. To this end, we develop a Bayesian composition model for forecasting how individual athletes perform in each of the 10 decathlon events of time. Besides capturing potential non-linear temporal trends in performance, our model carefully captures the dependence between performance in an event and all preceding events. Using our model, we can simulate and evaluate the distribution of the maximal possible scores and identify profiles of athletes who could realistically attain scores approaching this limit.
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。
