TY - JOUR
T1 - Informational predictability, and an application to the intensity of high-resolution temporal rainfall
AU - Fernández Méndez, Félix
AU - Gómez Larrañaga, José Carlos
AU - Carsteanu, Alin Andrei
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2023/7
Y1 - 2023/7
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85149394051&partnerID=8YFLogxK
U2 - 10.1007/s00477-023-02410-7
DO - 10.1007/s00477-023-02410-7
M3 - Artículo
AN - SCOPUS:85149394051
SN - 1436-3240
VL - 37
SP - 2651
EP - 2656
JO - Stochastic Environmental Research and Risk Assessment
JF - Stochastic Environmental Research and Risk Assessment
IS - 7
ER -