Autor segons l'article: Guimera, Roger; Reichardt, Ignasi; Aguilar-Mogas, Antoni; Massucci, Francesco A; Miranda, Manuel; Pallares, Jordi; Sales-Pardo, Marta
Departament: Enginyeria Química
Autor/s de la URV: Guimera Manrique, Roger / MASSUCCI, FRANCESCO ALESSANDRO / MIRANDA GUARDIOLA, MERCEDES / Pallarés Curto, Jorge María / Pallarès Marzal, Josep / Sales Pardo, Marta
Paraules clau: Models Equation
Resum: 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.
Àrees temàtiques: 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
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
ISSN: 2375-2548
Adreça de correu electrònic de l'autor: roger.guimera@urv.cat josep.pallares@urv.cat jordi.pallares@urv.cat marta.sales@urv.cat
Identificador de l'autor: 0000-0002-3597-4310 0000-0001-7221-5383 0000-0003-0305-2714 0000-0002-8140-6525
Data d'alta del registre: 2024-10-19
Volum de revista: 6
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://advances.sciencemag.org/content/6/5/eaav6971
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Science Advances. 6 (5): eaav6971-
Referència de l'ítem segons les normes APA: Guimera, Roger; Reichardt, Ignasi; Aguilar-Mogas, Antoni; Massucci, Francesco A; Miranda, Manuel; Pallares, Jordi; Sales-Pardo, Marta (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 de l'article: 10.1126/sciadv.aav6971
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2020
Tipus de publicació: Journal Publications