Autor según el artículo: Piga, Angelo; Font-Pomarol, Lluc; Sales-Pardo, Marta; Guimera, Roger
Departamento: Enginyeria Química
Autor/es de la URV: Font Pomarol, Lluc / Guimera Manrique, Roger / Piga, Angelo / Sales Pardo, Marta
Palabras clave: Bayesian estimation Entropy estimation Inferenc Information theor Information theory Kullback-leibler divergence Kullback–leibler divergence Shannon entropy Sparse sampling
Resumen: 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.
Áreas temáticas: 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
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: marta.sales@urv.cat lluc.fonti@estudiants.urv.cat lluc.fonti@estudiants.urv.cat roger.guimera@urv.cat
Identificador del autor: 0000-0002-8140-6525 0000-0002-3597-4310
Fecha de alta del registro: 2024-10-19
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Enlace a la fuente original: https://www.sciencedirect.com/science/article/pii/S0960077924001152?via%3Dihub
Referencia al articulo segun fuente origial: Chaos Solitons & Fractals. 180 114564-
Referencia 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 Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
DOI del artículo: 10.1016/j.chaos.2024.114564
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2024
Tipo de publicación: Journal Publications