Articles producció científica> Enginyeria Química

A Bayesian machine scientist to aid in the solution of challenging scientific problems

  • Identification data

    Identifier: imarina:6389788
    Authors:
    Guimera, RogerReichardt, IgnasiAguilar-Mogas, AntoniMassucci, Francesco AMiranda, ManuelPallares, JordiSales-Pardo, Marta
    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.
  • Others:

    Author, as appears in the article.: Guimera, Roger; Reichardt, Ignasi; Aguilar-Mogas, Antoni; Massucci, Francesco A; Miranda, Manuel; Pallares, Jordi; Sales-Pardo, Marta
    Department: Enginyeria Química
    URV's Author/s: Guimera Manrique, Roger / MASSUCCI, FRANCESCO ALESSANDRO / MIRANDA GUARDIOLA, MERCEDES / Pallarés Curto, Jorge María / Pallarès Marzal, Josep / 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 josep.pallares@urv.cat jordi.pallares@urv.cat marta.sales@urv.cat
    Author identifier: 0000-0002-3597-4310 0000-0001-7221-5383 0000-0003-0305-2714 0000-0002-8140-6525
    Record's date: 2024-10-19
    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: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Science Advances. 6 (5): eaav6971-
    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
    Article's DOI: 10.1126/sciadv.aav6971
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2020
    Publication Type: Journal Publications
  • Keywords:

    Medicine (Miscellaneous),Multidisciplinary,Multidisciplinary Sciences
    Models
    Equation
    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
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