Energy markets are typically characterized by high complexity due to several reasons such as the large number of occurring variables, different in nature, and their associative structure. Estimating a statistical model that properly represents the dependencies among the variables is crucial for managing the complexity. In this paper the Colombian energy market is studied. Since the variables of interest are quantitative but non Gaussian, non parametric Bayesian networks are used to infer the Colombian energy market association structure.

Vitale, V., Guizzi, V., Musella, F., Vicard, P. (2016). Non-parametric Bayesian Networks for Managing an Energy Market. In Proceedings of the 48th SIS Scientific Meeting of the Italian Statistical Society.

Non-parametric Bayesian Networks for Managing an Energy Market

VITALE, VINCENZINA;GUIZZI, VALENTINA;VICARD, Paola
2016-01-01

Abstract

Energy markets are typically characterized by high complexity due to several reasons such as the large number of occurring variables, different in nature, and their associative structure. Estimating a statistical model that properly represents the dependencies among the variables is crucial for managing the complexity. In this paper the Colombian energy market is studied. Since the variables of interest are quantitative but non Gaussian, non parametric Bayesian networks are used to infer the Colombian energy market association structure.
9788861970618
Vitale, V., Guizzi, V., Musella, F., Vicard, P. (2016). Non-parametric Bayesian Networks for Managing an Energy Market. In Proceedings of the 48th SIS Scientific Meeting of the Italian Statistical Society.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/307524
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