2016
Valera, Isabel; Ruiz, Francisco J R; Perez-Cruz, Fernando
Infinite Factorial Unbounded-State Hidden Markov Model Artículo de revista
En: IEEE transactions on pattern analysis and machine intelligence, vol. 38, no 9, pp. 1816 – 1828, 2016, ISSN: 1939-3539.
Resumen | Enlaces | BibTeX | Etiquetas: Bayes methods, Bayesian nonparametrics, CASI CAM CM, Computational modeling, GAMMA-L+ UC3M, Gibbs sampling, Hidden Markov models, Inference algorithms, Journal, Markov processes, Probability distribution, reversible jump Markov chain Monte Carlo, slice sampling, Time series, variational inference, Yttrium
@article{Valera2016b,
title = {Infinite Factorial Unbounded-State Hidden Markov Model},
author = {Isabel Valera and Francisco J R Ruiz and Fernando Perez-Cruz},
url = {http://www.ncbi.nlm.nih.gov/pubmed/26571511 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true\&arnumber=7322279},
doi = {10.1109/TPAMI.2015.2498931},
issn = {1939-3539},
year = {2016},
date = {2016-09-01},
journal = {IEEE transactions on pattern analysis and machine intelligence},
volume = {38},
number = {9},
pages = {1816 -- 1828},
abstract = {There are many scenarios in artificial intelligence, signal processing or medicine, in which a temporal sequence consists of several unknown overlapping independent causes, and we are interested in accurately recovering those canonical causes. Factorial hidden Markov models (FHMMs) present the versatility to provide a good fit to these scenarios. However, in some scenarios, the number of causes or the number of states of the FHMM cannot be known or limited a priori. In this paper, we propose an infinite factorial unbounded-state hidden Markov model (IFUHMM), in which the number of parallel hidden Markov models (HMMs) and states in each HMM are potentially unbounded. We rely on a Bayesian nonparametric (BNP) prior over integer-valued matrices, in which the columns represent the Markov chains, the rows the time indexes, and the integers the state for each chain and time instant. First, we extend the existent infinite factorial binary-state HMM to allow for any number of states. Then, we modify this model to allow for an unbounded number of states and derive an MCMC-based inference algorithm that properly deals with the trade-off between the unbounded number of states and chains. We illustrate the performance of our proposed models in the power disaggregation problem.},
keywords = {Bayes methods, Bayesian nonparametrics, CASI CAM CM, Computational modeling, GAMMA-L+ UC3M, Gibbs sampling, Hidden Markov models, Inference algorithms, Journal, Markov processes, Probability distribution, reversible jump Markov chain Monte Carlo, slice sampling, Time series, variational inference, Yttrium},
pubstate = {published},
tppubtype = {article}
}
Valera, Isabel; Ruiz, Francisco J R; Perez-Cruz, Fernando
Infinite Factorial Unbounded-State Hidden Markov Model Artículo de revista
En: IEEE transactions on pattern analysis and machine intelligence, vol. To appear, no 99, pp. 1, 2016, ISSN: 1939-3539.
Resumen | Enlaces | BibTeX | Etiquetas: Bayes methods, Bayesian nonparametrics, CASI CAM CM, Computational modeling, GAMMA-L+ UC3M, Gibbs sampling, Hidden Markov models, Inference algorithms, Markov processes, Probability distribution, reversible jump Markov chain Monte Carlo, slice sampling, Time series, variational inference, Yttrium
@article{Valera2016c,
title = {Infinite Factorial Unbounded-State Hidden Markov Model},
author = {Isabel Valera and Francisco J R Ruiz and Fernando Perez-Cruz},
url = {http://www.ncbi.nlm.nih.gov/pubmed/26571511 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true\&arnumber=7322279},
doi = {10.1109/TPAMI.2015.2498931},
issn = {1939-3539},
year = {2016},
date = {2016-01-01},
journal = {IEEE transactions on pattern analysis and machine intelligence},
volume = {To appear},
number = {99},
pages = {1},
abstract = {There are many scenarios in artificial intelligence, signal processing or medicine, in which a temporal sequence consists of several unknown overlapping independent causes, and we are interested in accurately recovering those canonical causes. Factorial hidden Markov models (FHMMs) present the versatility to provide a good fit to these scenarios. However, in some scenarios, the number of causes or the number of states of the FHMM cannot be known or limited a priori. In this paper, we propose an infinite factorial unbounded-state hidden Markov model (IFUHMM), in which the number of parallel hidden Markov models (HMMs) and states in each HMM are potentially unbounded. We rely on a Bayesian nonparametric (BNP) prior over integer-valued matrices, in which the columns represent the Markov chains, the rows the time indexes, and the integers the state for each chain and time instant. First, we extend the existent infinite factorial binary-state HMM to allow for any number of states. Then, we modify this model to allow for an unbounded number of states and derive an MCMC-based inference algorithm that properly deals with the trade-off between the unbounded number of states and chains. We illustrate the performance of our proposed models in the power disaggregation problem.},
keywords = {Bayes methods, Bayesian nonparametrics, CASI CAM CM, Computational modeling, GAMMA-L+ UC3M, Gibbs sampling, Hidden Markov models, Inference algorithms, Markov processes, Probability distribution, reversible jump Markov chain Monte Carlo, slice sampling, Time series, variational inference, Yttrium},
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}
}
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}
}