Informational predictability, and an application to the intensity of high-resolution temporal rainfall

In an effort to solidly define predictability, and the estimation thereof, in different processes, and considering the interpretation of mutual information as an expected value of the logarithm of joint probability, we show that, in the light of causality, the expected value of the logarithm of cond...

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Veröffentlicht in:	Stochastic environmental research and risk assessment 2023-07, Vol.37 (7), p.2651-2656
Hauptverfasser:	Fernández Méndez, Félix, Gómez Larrañaga, José Carlos, Carsteanu, Alin Andrei
Format:	Artikel
Sprache:	eng
Schlagworte:	Aquatic Pollution Chemistry and Earth Sciences Codes Computational Intelligence Computer Science Conditional probability Earth and Environmental Science Earth Sciences Environment Environmental research Expected values High resolution Logarithms Math. Appl. in Environmental Science Original Paper Physics Probability Probability distribution Probability Theory and Stochastic Processes rain rain intensity Rainfall Rainfall intensity Random variables risk Risk assessment Statistics for Engineering time series analysis Waste Water Technology Water Management Water Pollution Control
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Zusammenfassung:	In an effort to solidly define predictability, and the estimation thereof, in different processes, and considering the interpretation of mutual information as an expected value of the logarithm of joint probability, we show that, in the light of causality, the expected value of the logarithm of conditional probability fulfills the desirable properties of a predictability descriptor. An application to high-resolution rainfall intensity time series is presented.
ISSN:	1436-3240 1436-3259
DOI:	10.1007/s00477-023-02410-7