Autor segons l'article: Piga, Angelo; Font-Pomarol, Lluc; Sales-Pardo, Marta; Guimera, Roger
Departament: Enginyeria Química
Autor/s de la URV: Font Pomarol, Lluc / Guimera Manrique, Roger / Piga, Angelo / Sales Pardo, Marta
Paraules clau: Bayesian estimation Entropy estimation Inferenc Information theor Information theory Kullback-leibler divergence Kullback–leibler divergence Shannon entropy Sparse sampling
Resum: Estimating the Shannon entropy of a discrete distribution from which we have only observed a small sample is challenging. Estimating other information-theoretic metrics, such as the Kullback-Leibler divergence between two sparsely sampled discrete distributions, is even harder. Here, we propose a fast, semi-analytical estimator for sparsely sampled distributions. Its derivation is grounded in probabilistic considerations and uses a hierarchical Bayesian approach to extract as much information as possible from the few observations available. Our approach provides estimates of the Shannon entropy with precision at least comparable to the benchmarks we consider, and most often higher; it does so across diverse distributions with very different properties. Our method can also be used to obtain accurate estimates of other information-theoretic metrics, including the notoriously challenging Kullback-Leibler divergence. Here, again, our approach has less bias, overall, than the benchmark estimators we consider.
Àrees temàtiques: Applied mathematics Astronomia / física Ciência da computação Ciências biológicas i Ciências biológicas ii Direito Economia Engenharias i Engenharias ii Engenharias iii Engenharias iv General mathematics General physics and astronomy Geociências Interdisciplinar Matemática / probabilidade e estatística Materiais Mathematical physics Mathematics (all) Mathematics (miscellaneous) Mathematics, applied Mathematics, interdisciplinary applications Physics Physics and astronomy (all) Physics and astronomy (miscellaneous) Physics, mathematical Physics, multidisciplinary Química Statistical and nonlinear physics
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: marta.sales@urv.cat lluc.fonti@estudiants.urv.cat lluc.fonti@estudiants.urv.cat roger.guimera@urv.cat
Identificador de l'autor: 0000-0002-8140-6525 0000-0002-3597-4310
Data d'alta del registre: 2024-10-19
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://www.sciencedirect.com/science/article/pii/S0960077924001152?via%3Dihub
Referència a l'article segons font original: Chaos Solitons & Fractals. 180 114564-
Referència de l'ítem segons les normes APA: Piga, Angelo; Font-Pomarol, Lluc; Sales-Pardo, Marta; Guimera, Roger (2024). Bayesian estimation of information-theoretic metrics for sparsely sampled distributions. Chaos Solitons & Fractals, 180(), 114564-. DOI: 10.1016/j.chaos.2024.114564
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
DOI de l'article: 10.1016/j.chaos.2024.114564
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2024
Tipus de publicació: Journal Publications