2019
Pérez-Cruz, Fernando; Olmos, Pablo M; Zhang, Michael Minyi; Huang, Howard
Probabilistic Time of Arrival Localization Artículo de revista
En: IEEE Signal Processing Letters, vol. 26, no 11, pp. 1683 - 1687, 2019.
Enlaces | BibTeX | Etiquetas: Probabilistic modeling, Time of arrival localization
@article{FPerez18,
title = {Probabilistic Time of Arrival Localization},
author = {Fernando P\'{e}rez-Cruz and Pablo M Olmos and Michael Minyi Zhang and Howard Huang},
doi = {10.1109/LSP.2019.2944005},
year = {2019},
date = {2019-09-26},
journal = {IEEE Signal Processing Letters},
volume = {26},
number = {11},
pages = {1683 - 1687},
keywords = {Probabilistic modeling, Time of arrival localization},
pubstate = {published},
tppubtype = {article}
}
2015
Ruiz, Francisco J R; Perez-Cruz, Fernando
A Generative Model for Predicting Outcomes in College Basketball Artículo de revista
En: Journal of Quantitative Analysis in Sports, vol. 11, no 1 Special Issue, pp. 39–52, 2015, ISSN: 1559-0410.
Resumen | Enlaces | BibTeX | Etiquetas: CASI CAM CM, GAMMA-L+ UC3M, Journal, NCAA tournament, Poisson factorization, Probabilistic modeling, variational inference
@article{Ruiz2015b,
title = {A Generative Model for Predicting Outcomes in College Basketball},
author = {Francisco J R Ruiz and Fernando Perez-Cruz},
url = {http://www.degruyter.com/view/j/jqas.2015.11.issue-1/jqas-2014-0055/jqas-2014-0055.xml},
doi = {10.1515/jqas-2014-0055},
issn = {1559-0410},
year = {2015},
date = {2015-03-01},
journal = {Journal of Quantitative Analysis in Sports},
volume = {11},
number = {1 Special Issue},
pages = {39--52},
abstract = {We show that a classical model for soccer can also provide competitive results in predicting basketball outcomes. We modify the classical model in two ways in order to capture both the specific behavior of each National collegiate athletic association (NCAA) conference and different strategies of teams and conferences. Through simulated bets on six online betting houses, we show that this extension leads to better predictive performance in terms of profit we make. We compare our estimates with the probabilities predicted by the winner of the recent Kaggle competition on the 2014 NCAA tournament, and conclude that our model tends to provide results that differ more from the implicit probabilities of the betting houses and, therefore, has the potential to provide higher benefits.},
keywords = {CASI CAM CM, GAMMA-L+ UC3M, Journal, NCAA tournament, Poisson factorization, Probabilistic modeling, variational inference},
pubstate = {published},
tppubtype = {article}
}
We show that a classical model for soccer can also provide competitive results in predicting basketball outcomes. We modify the classical model in two ways in order to capture both the specific behavior of each National collegiate athletic association (NCAA) conference and different strategies of teams and conferences. Through simulated bets on six online betting houses, we show that this extension leads to better predictive performance in terms of profit we make. We compare our estimates with the probabilities predicted by the winner of the recent Kaggle competition on the 2014 NCAA tournament, and conclude that our model tends to provide results that differ more from the implicit probabilities of the betting houses and, therefore, has the potential to provide higher benefits.
Ruiz, Francisco J R; Perez-Cruz, Fernando
A Generative Model for Predicting Outcomes in College Basketball Artículo de revista
En: Journal of Quantitative Analysis in Sports, vol. 11, no 1 Special Issue, pp. 39–52, 2015, ISSN: 1559-0410.
Resumen | Enlaces | BibTeX | Etiquetas: CASI CAM CM, GAMMA-L+ UC3M, NCAA tournament, Poisson factorization, Probabilistic modeling, variational inference
@article{Ruiz2015bb,
title = {A Generative Model for Predicting Outcomes in College Basketball},
author = {Francisco J R Ruiz and Fernando Perez-Cruz},
url = {http://www.degruyter.com/view/j/jqas.2015.11.issue-1/jqas-2014-0055/jqas-2014-0055.xml},
doi = {10.1515/jqas-2014-0055},
issn = {1559-0410},
year = {2015},
date = {2015-03-01},
journal = {Journal of Quantitative Analysis in Sports},
volume = {11},
number = {1 Special Issue},
pages = {39--52},
abstract = {We show that a classical model for soccer can also provide competitive results in predicting basketball outcomes. We modify the classical model in two ways in order to capture both the specific behavior of each National collegiate athletic association (NCAA) conference and different strategies of teams and conferences. Through simulated bets on six online betting houses, we show that this extension leads to better predictive performance in terms of profit we make. We compare our estimates with the probabilities predicted by the winner of the recent Kaggle competition on the 2014 NCAA tournament, and conclude that our model tends to provide results that differ more from the implicit probabilities of the betting houses and, therefore, has the potential to provide higher benefits.},
keywords = {CASI CAM CM, GAMMA-L+ UC3M, NCAA tournament, Poisson factorization, Probabilistic modeling, variational inference},
pubstate = {published},
tppubtype = {article}
}
We show that a classical model for soccer can also provide competitive results in predicting basketball outcomes. We modify the classical model in two ways in order to capture both the specific behavior of each National collegiate athletic association (NCAA) conference and different strategies of teams and conferences. Through simulated bets on six online betting houses, we show that this extension leads to better predictive performance in terms of profit we make. We compare our estimates with the probabilities predicted by the winner of the recent Kaggle competition on the 2014 NCAA tournament, and conclude that our model tends to provide results that differ more from the implicit probabilities of the betting houses and, therefore, has the potential to provide higher benefits.