Autor según el artículo: Guimerà R; Reichardt I; Aguilar-Mogas A; Massucci FA; Miranda M; Pallarès J; Sales-Pardo M

Departamento: Enginyeria Química

Autor/es de la URV: Guimera Manrique, Roger / MASSUCCI, FRANCESCO ALESSANDRO / Pallarés Curto, Jorge María / Sales Pardo, Marta

Palabras clave: Models Equation

Resumen: Copyright © 2020 The Authors, some rights reserved. Closed-form, interpretable mathematical models have been instrumental for advancing our understanding of the world; with the data revolution, we may now be in a position to uncover new such models for many systems from physics to the social sciences. However, to deal with increasing amounts of data, we need machine scientists that are able to extract these models automatically from data. Here, we introduce a Bayesian machine scientist, which establishes the plausibility of models using explicit approximations to the exact marginal posterior over models and establishes its prior expectations about models by learning from a large empirical corpus of mathematical expressions. It explores the space of models using Markov chain Monte Carlo. We show that this approach uncovers accurate models for synthetic and real data and provides out-of-sample predictions that are more accurate than those of existing approaches and of other nonparametric methods.

Áreas temáticas: Química Multidisciplinary sciences Multidisciplinary Medicine (miscellaneous) Interdisciplinar Geociências General medicine Engenharias iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Biotecnología Biodiversidade Astronomia / física

Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/

ISSN: 2375-2548

Direcció de correo del autor: roger.guimera@urv.cat marta.sales@urv.cat jordi.pallares@urv.cat

Identificador del autor: 0000-0002-3597-4310 0000-0002-8140-6525 0000-0003-0305-2714

Fecha de alta del registro: 2023-02-26

Volumen de revista: 6

Versión del articulo depositado: info:eu-repo/semantics/publishedVersion

Enlace a la fuente original: https://advances.sciencemag.org/content/6/5/eaav6971

URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/

Referencia al articulo segun fuente origial: Science Advances. 6 (5): eaav6971-

Referencia de l'ítem segons les normes APA: Guimerà R; Reichardt I; Aguilar-Mogas A; Massucci FA; Miranda M; Pallarès J; Sales-Pardo M (2020). A Bayesian machine scientist to aid in the solution of challenging scientific problems. Science Advances, 6(5), eaav6971-. DOI: 10.1126/sciadv.aav6971

DOI del artículo: 10.1126/sciadv.aav6971

Entidad: Universitat Rovira i Virgili

Año de publicación de la revista: 2020

Tipo de publicación: Journal Publications