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A Bayesian inference and model selection algorithm with an optimization scheme to infer the model noise power

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dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Tratamiento de la Señal y Aprendizaje (GTSA)es
dc.contributor.authorLópez Santiago, Javier
dc.contributor.authorMartino, Luca
dc.contributor.authorVázquez López, Manuel Alberto
dc.contributor.authorMíguez Arenas, Joaquín
dc.contributor.funderComunidad de Madrides
dc.contributor.funderMinisterio de Ciencia e Innovación (España)es
dc.date.accessioned2023-12-12T12:40:44Z
dc.date.available2023-12-12T12:40:44Z
dc.date.issued2021-11
dc.description.abstractModel fitting is possibly the most extended problem in science. Classical approaches include the use of least-squares fitting procedures and maximum likelihood methods to estimate the value of the parameters in the model. However, in recent years, Bayesian inference tools have gained traction. Usually, Markov chain Monte Carlo (MCMC) methods are applied to inference problems, but they present some disadvantages, particularly when comparing different models fitted to the same data set. Other Bayesian methods can deal with this issue in a natural and effective way. We have implemented an importance sampling (IS) algorithm adapted to Bayesian inference problems in which the power of the noise in the observations is not known a priori. The main advantage of IS is that the model evidence can be derived directly from the so-called importance weights - while MCMC methods demand considerable postprocessing. The use of our adaptive target adaptive importance sampling (ATAIS) method is shown by inferring, on the one hand, the parameters of a simulated flaring event that includes a damped oscillation and, on the other hand, real data from the Kepler mission. ATAIS includes a novel automatic adaptation of the target distribution. It automatically estimates the variance of the noise in the model. ATAIS admits parallelization, which decreases the computational run-times notably. We compare our method against a nested sampling method within a model selection problem.en
dc.description.sponsorshipThis work was supported by the Office of Naval Research (N00014-19-1-2226), Spanish Ministry of Science and Innovation (CLARA; RTI2018-099655-B-I00), and Regional Ministry of Education and Research for the Community of Madrid (PRACTICO; Y2018/TCS4705). This paper includes data collected by the Kepler mission and obtained from the MAST data archive at the Space Telescope Science Institute (STScI). Funding for the Kepler mission is provided by the NASA Science Mission Directorate. STScI is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. The authors acknowledges fruitful discussion with the referee to improve this paper.en
dc.format.extent11
dc.identifier.bibliographicCitationLópez‐Santiago, J., Martino, L., Vázquez, M. A., & Mı́guez, J. (2021). A Bayesian inference and model selection algorithm with an optimization scheme to infer the model noise power. Monthly Notices of the Royal Astronomical Society, 507(3), 3351-3361.en
dc.identifier.doihttps://doi.org/10.1093/mnras/stab2303
dc.identifier.issn0035-8711
dc.identifier.publicationfirstpage3351
dc.identifier.publicationissue3
dc.identifier.publicationlastpage3361
dc.identifier.publicationtitleMonthly Notices of the Royal Astronomical Societyen
dc.identifier.publicationvolume507
dc.identifier.urihttps://hdl.handle.net/10016/39064
dc.identifier.uxxiAR/0000028497
dc.language.isoengen
dc.publisherOxford Academicen
dc.relation.projectIDGobierno de España. RTI2018-099655-B-I00es
dc.relation.projectIDComunidad de Madrid. Y2018/TCS4705es
dc.rights© 2021 The Author(s)en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaEconomíaes
dc.subject.ecienciaInformáticaes
dc.subject.ecienciaMatemáticases
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherMethods: Data Analysisen
dc.subject.otherMethods: Numericalen
dc.subject.otherMethods: Statisticalen
dc.subject.otherStars: Activityen
dc.subject.otherStars: Flareen
dc.titleA Bayesian inference and model selection algorithm with an optimization scheme to infer the model noise poweren
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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