Articolo in rivista, 2022, ENG, 10.1155/2022/9518303

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

Murari A.; Lungaroni M.; Rossi R.; Spolladore L.; Gelfusa M.

CNR ISTP - Istituto per la Scienza e Tecnologia dei Plasmi, Sede di Padova, Italy; Consorzio RFX (CNR, ENEA, INFN, Universita' di Padova, Acciaierie Venete SpA), Padova, Italy; University of Rome Tor Vergata, Department of Industrial Engineering, Roma, Italy.

It can be argued that the identification of sound mathematical models is the ultimate goal of any scientific endeavour. On the other hand, particularly in the investigation of complex systems and nonlinear phenomena, discriminating between alternative models can be a very challenging task. Quite sophisticated model selection criteria are available but their deployment in practice can be problematic. In this work, the Akaike Information Criterion is reformulated with the help of purely information theoretic quantities, namely, the Gibbs-Shannon entropy and the Mutual Information. Systematic numerical tests have proven the improved performances of the proposed upgrades, including increased robustness against noise and the presence of outliers. The same modifications can be implemented to rewrite also Bayesian statistical criteria, such as the Schwartz indicator, in terms of information-theoretic quantities, proving the generality of the approach and the validity of the underlying assumptions.

Complexity (Online) , pp. 9518303-1–9518303-12

Keywords

Analysis of Experimental Data, Data-Driven Methods

CNR authors

Murari Andrea

CNR institutes

ISTP – Istituto per la Scienza e Tecnologia dei Plasmi