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

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 ag...

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Detalles Bibliográficos
Autores principales: F. Pradier, Melanie, J. R. Ruiz, Francisco, Perez-Cruz, Fernando
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4731079/
https://www.ncbi.nlm.nih.gov/pubmed/26821155
http://dx.doi.org/10.1371/journal.pone.0147402

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4731079/
https://www.ncbi.nlm.nih.gov/pubmed/26821155
http://dx.doi.org/10.1371/journal.pone.0147402

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