Prior Design for Dependent Dirichlet Processes: An Application to Marathon Modeling

PLoS One

Francisco J. R. Ruiz¹  Fernando Perez-Cruz²  Melanie F. Pradier² 

[1] Gregorio Marañón Health Research Institute, Madrid, Spain;Signal Theory and Communications Department, University Carlos III in Madrid, Madrid, Spain

关键词: Sports;    Age groups;    Stochastic processes;    Running;    Behavior;    Covariance;    Density;    Mathematical functions;   

DOI  :  10.1371/journal.pone.0147402

学科分类：医学（综合）

来源: Public Library of Science

【 摘 要 】

This paper presents a novel application of Bayesian nonparametrics (BNP) for marathon data modeling. We make use of two well-known BNP priors, the single-p dependent Dirichlet process and the hierarchical Dirichlet process, in order to address two different problems. First, we study the impact of age, gender and environment on the runners’ performance. We derive a fair grading method that allows direct comparison of runners regardless of their age and gender. Unlike current grading systems, our approach is based not only on top world records, but on the performances of all runners. The presented methodology for comparison of densities can be adopted in many other applications straightforwardly, providing an interesting perspective to build dependent Dirichlet processes. Second, we analyze the running patterns of the marathoners in time, obtaining information that can be valuable for training purposes. We also show that these running patterns can be used to predict finishing time given intermediate interval measurements. We apply our models to New York City, Boston and London marathons.

【 授权许可】

CC BY