Complexity: Frontiers in Data-Driven Methods for Understanding, Prediction, and Control of Complex Systems 2022 on the Development of Information Theoretic Model Selection Criteria for the Analysis of Experimental Data | Zendy

A. Murari | Zendy; M. Lungaroni | Zendy; Riccardo Rossi | Zendy; L. Spolladore | Zendy; M. Gelfusa | Zendy

Open Access

Complexity: Frontiers in Data-Driven Methods for Understanding, Prediction, and Control of Complex Systems 2022 on the Development of Information Theoretic Model Selection Criteria for the Analysis of Experimental Data

Author(s) -

A. Murari,

M. Lungaroni,

Riccardo Rossi,

L. Spolladore,

M. Gelfusa

Publication year - 2022

Publication title -

complexity

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.447

H-Index - 61

eISSN - 1099-0526

pISSN - 1076-2787

DOI - 10.1155/2022/9518303

Subject(s) - akaike information criterion , generality , computer science , outlier , bayesian information criterion , model selection , information theory , robustness (evolution) , mutual information , entropy (arrow of time) , data mining , machine learning , artificial intelligence , mathematics , statistics , psychology , biochemistry , chemistry , physics , quantum mechanics , psychotherapist , gene

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