Author, as appears in the article.: Guimerà R; Reichardt I; Aguilar-Mogas A; Massucci FA; Miranda M; Pallarès J; Sales-Pardo M
Department: Enginyeria Química
URV's Author/s: Guimera Manrique, Roger / MASSUCCI, FRANCESCO ALESSANDRO / Pallarés Curto, Jorge María / Sales Pardo, Marta
Keywords: Models Equation
Abstract: 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.
Thematic Areas: 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
licence for use: https://creativecommons.org/licenses/by/3.0/es/
ISSN: 2375-2548
Author's mail: roger.guimera@urv.cat marta.sales@urv.cat jordi.pallares@urv.cat
Author identifier: 0000-0002-3597-4310 0000-0002-8140-6525 0000-0003-0305-2714
Record's date: 2023-02-26
Journal volume: 6
Papper version: info:eu-repo/semantics/publishedVersion
Link to the original source: https://advances.sciencemag.org/content/6/5/eaav6971
Licence document URL: http://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Science Advances. 6 (5): eaav6971-
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
Article's DOI: 10.1126/sciadv.aav6971
Entity: Universitat Rovira i Virgili
Journal publication year: 2020
Publication Type: Journal Publications
Repositori URV