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Mixture of noises and sampling of non-log-concave posterior distributions

hal.structure.identifierCentre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
hal.structure.identifierLaboratoire d'Etude du Rayonnement et de la Matière en Astrophysique et Atmosphères = Laboratory for Studies of Radiation and Matter in Astrophysics and Atmospheres [LERMA]
dc.contributor.authorPALUD, Pierre
hal.structure.identifierCentre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
dc.contributor.authorCHAINAIS, Pierre
hal.structure.identifierLaboratoire d'Etude du Rayonnement et de la Matière en Astrophysique et Atmosphères = Laboratory for Studies of Radiation and Matter in Astrophysics and Atmospheres [LERMA]
dc.contributor.authorLE PETIT, Franck
hal.structure.identifierLaboratoire d'Etude du Rayonnement et de la Matière en Astrophysique et Atmosphères = Laboratory for Studies of Radiation and Matter in Astrophysics and Atmospheres [LERMA]
dc.contributor.authorBRON, Emeric
hal.structure.identifierCriteo AI Lab
dc.contributor.authorVONO, Maxime
hal.structure.identifierInstitut de RadioAstronomie Millimétrique [IRAM]
dc.contributor.authorEINIG, Lucas
hal.structure.identifierInstituto de Física Fundamental [Madrid] [IFF]
dc.contributor.authorGARCIA SANTA-MARIA, Miriam
hal.structure.identifierLaboratoire d'Etude du Rayonnement et de la Matière en Astrophysique [LERMA]
dc.contributor.authorGAUDEL, Mathilde
hal.structure.identifierDepartment of Space, Earth and Environment, Chalmers University of Technology
dc.contributor.authorORKISZ, Jan
hal.structure.identifierInstitut de RadioAstronomie Millimétrique [IRAM]
dc.contributor.authorMAGALHAES, Victor De Souza
hal.structure.identifierObservatoire aquitain des sciences de l'univers [OASU]
hal.structure.identifierLaboratoire d'Astrophysique de Bordeaux [Pessac] [LAB]
dc.contributor.authorBARDEAU, Sébastien
hal.structure.identifierLaboratoire d'Etude du Rayonnement et de la Matière en Astrophysique [LERMA]
dc.contributor.authorGERIN, Maryvonne
hal.structure.identifierInstituto de Física Fundamental [Madrid] [IFF]
dc.contributor.authorGOICOECHEA, Javier
hal.structure.identifierObservatoire aquitain des sciences de l'univers [OASU]
hal.structure.identifierUniversité Sciences et Technologies - Bordeaux 1 [UB]
hal.structure.identifierLaboratoire d'Astrophysique de Bordeaux [Pessac] [LAB]
hal.structure.identifierLaboratoire d'astrodynamique, d'astrophysique et d'aéronomie de bordeaux [L3AB]
dc.contributor.authorGRATIER, Pierre
hal.structure.identifierJoint ALMA Observatory [JAO]
dc.contributor.authorGUZMAN, Viviana
hal.structure.identifierDepartment of Space, Earth and Environment, Chalmers University of Technology
dc.contributor.authorKAINULAINEN, Jouni
hal.structure.identifierLaboratoire de Radioastronomie
dc.contributor.authorLEVRIER, François
hal.structure.identifierSchool of Physics and Astronomy [Cardiff]
hal.structure.identifierAstrophysique Interprétation Modélisation [AIM (UMR7158 / UMR_E_9005 / UM_112)]
dc.contributor.authorPERETTO, Nicolas
hal.structure.identifierInstitut de RadioAstronomie Millimétrique [IRAM]
dc.contributor.authorPETY, Jérôme
hal.structure.identifierPhyTI [PhyTI]
dc.contributor.authorROUEFF, Antoine
hal.structure.identifierInstituto de RadioAstronomía Milimétrica [IRAM]
dc.contributor.authorSIEVERS, Albrecht
dc.date.conference2022-08-29
dc.description.abstractEnThis work considers a challenging radio-astronomyinverse problem of physical parameter inference from multispec-tral observations. The forward model underlying this problem isa computationally expensive numerical simulation. In addition,the observation model mixes different sources of noise yieldinga non-concave log-likelihood function. To overcome these issues,we introduce a likelihood approximation with controlled error.Given the absence of ground truth, parameter inference isconducted with a Markov chain Monte Carlo (MCMC) algorithmto provide credibility intervals along with point estimates. To thisaim, we propose a new sampler that addresses the numericalchallenges induced by the observation model, in particular thenon-log-concavity of the posterior distribution. The efficiency ofthe proposed method is demonstrated on synthetic yet realisticastrophysical data. We believe that the proposed approach isvery general and can be adapted to many similar difficult inverseproblems
dc.description.sponsorshipInférence rapide et contrôle de l'incertitude: applications aux observations astrophysiques.
dc.description.sponsorshipULNE - ANR-16-IDEX-0004
dc.language.isoen
dc.subject.enInverse problem
dc.subject.enBayesian inference
dc.subject.enMarkov chain Monte Carlo algorithm
dc.subject.enmultiplicative noise
dc.title.enMixture of noises and sampling of non-log-concave posterior distributions
dc.typeCommunication dans un congrès
dc.subject.halInformatique [cs]/Traitement du signal et de l'image
bordeaux.conference.title2022 30th European Signal Processing Conference (EUSIPCO)
bordeaux.countryRS
bordeaux.conference.cityBelgrade
bordeaux.peerReviewedoui
hal.identifierhal-03953035
hal.version1
hal.invitednon
hal.proceedingsnon
hal.conference.end2022-09-02
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03953035v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.au=PALUD,%20Pierre&CHAINAIS,%20Pierre&LE%20PETIT,%20Franck&BRON,%20Emeric&VONO,%20Maxime&rft.genre=unknown


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