Bayesian approach to the inverse problem in a light scattering application

Autores
Otero, Fernando Agustín; Barreto Orlande, Helcio R.; Frontini, Gloria Lia; Elicabe, Guillermo Enrique
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this article, static light scattering (SLS) measurements are processed to estimate the particle size distribution of particle systems incorporating prior information obtained from an alternative experimental technique: scanning electron microscopy (SEM). For this purpose we propose two Bayesian schemes (one parametric and another non-parametric) to solve the stated light scattering problem and take advantage of the obtained results to summarize some features of the Bayesian approach within the context of inverse problems. The features presented in this article include the improvement of the results when some useful prior information from an alternative experiment is considered instead of a non-informative prior as it occurs in a deterministic maximum likelihood estimation. This improvement will be shown in terms of accuracy and precision in the corresponding results and also in terms of minimizing the effect of multiple minima by including significant information in the optimization. Both Bayesian schemes are implemented using Markov Chain Monte Carlo methods. They have been developed on the basis of the Metropolis–Hastings (MH) algorithm using Matlab® and are tested with the analysis of simulated and experimental examples of concentrated and semi-concentrated particles. In the simulated examples, SLS measurements were generated using a rigorous model, while the inversion stage was solved using an approximate model in both schemes and also using the rigorous model in the parametric scheme. Priors from SEM micrographs were also simulated and experimented, where the simulated ones were obtained using a Monte Carlo routine. In addition to the presentation of these features of the Bayesian approach, some other topics will be discussed, such as regularization and some implementation issues of the proposed schemes, among which we remark the selection of the parameters used in the MH algorithm.
Fil: Otero, Fernando Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Mar del Plata. Instituto de Investigación En Ciencia y Tecnología de Materiales (i); Argentina. Universidad Nacional de Mar del Plata. Facultad de Ingeniería; Argentina
Fil: Barreto Orlande, Helcio R.. Universidade Federal Do Rio de Janeiro. Inst A.luiz Coimbra de Pos-graduacao E Pesquisa de Engenharia; Brasil
Fil: Frontini, Gloria Lia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Mar del Plata. Instituto de Investigación En Ciencia y Tecnología de Materiales (i); Argentina. Universidad Nacional de Mar del Plata. Facultad de Ingeniería; Argentina
Fil: Elicabe, Guillermo Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Mar del Plata. Instituto de Investigación En Ciencia y Tecnología de Materiales (i); Argentina. Universidad Nacional de Mar del Plata. Facultad de Ingeniería; Argentina
Materia
Bayesian Estimation
Particle Size Distribution
Inverse Problem
Metropolis-Hastings
Static Light Scattering
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/5055
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/5055
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Bayesian approach to the inverse problem in a light scattering applicationOtero, Fernando AgustínBarreto Orlande, Helcio R.Frontini, Gloria LiaElicabe, Guillermo EnriqueBayesian EstimationParticle Size DistributionInverse ProblemMetropolis-HastingsStatic Light Scatteringhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1In this article, static light scattering (SLS) measurements are processed to estimate the particle size distribution of particle systems incorporating prior information obtained from an alternative experimental technique: scanning electron microscopy (SEM). For this purpose we propose two Bayesian schemes (one parametric and another non-parametric) to solve the stated light scattering problem and take advantage of the obtained results to summarize some features of the Bayesian approach within the context of inverse problems. The features presented in this article include the improvement of the results when some useful prior information from an alternative experiment is considered instead of a non-informative prior as it occurs in a deterministic maximum likelihood estimation. This improvement will be shown in terms of accuracy and precision in the corresponding results and also in terms of minimizing the effect of multiple minima by including significant information in the optimization. Both Bayesian schemes are implemented using Markov Chain Monte Carlo methods. They have been developed on the basis of the Metropolis–Hastings (MH) algorithm using Matlab® and are tested with the analysis of simulated and experimental examples of concentrated and semi-concentrated particles. In the simulated examples, SLS measurements were generated using a rigorous model, while the inversion stage was solved using an approximate model in both schemes and also using the rigorous model in the parametric scheme. Priors from SEM micrographs were also simulated and experimented, where the simulated ones were obtained using a Monte Carlo routine. In addition to the presentation of these features of the Bayesian approach, some other topics will be discussed, such as regularization and some implementation issues of the proposed schemes, among which we remark the selection of the parameters used in the MH algorithm.Fil: Otero, Fernando Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Mar del Plata. Instituto de Investigación En Ciencia y Tecnología de Materiales (i); Argentina. Universidad Nacional de Mar del Plata. Facultad de Ingeniería; ArgentinaFil: Barreto Orlande, Helcio R.. Universidade Federal Do Rio de Janeiro. Inst A.luiz Coimbra de Pos-graduacao E Pesquisa de Engenharia; BrasilFil: Frontini, Gloria Lia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Mar del Plata. Instituto de Investigación En Ciencia y Tecnología de Materiales (i); Argentina. Universidad Nacional de Mar del Plata. Facultad de Ingeniería; ArgentinaFil: Elicabe, Guillermo Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Mar del Plata. Instituto de Investigación En Ciencia y Tecnología de Materiales (i); Argentina. Universidad Nacional de Mar del Plata. Facultad de Ingeniería; ArgentinaRoutledge Journals, Taylor & Francis Ltd2014-11-25info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/5055Otero, Fernando Agustín; Barreto Orlande, Helcio R.; Frontini, Gloria Lia; Elicabe, Guillermo Enrique; Bayesian approach to the inverse problem in a light scattering application; Routledge Journals, Taylor & Francis Ltd; Journal of Applied Statistics; 42; 5; 25-11-2014; 994-10160266-4763enginfo:eu-repo/semantics/altIdentifier/doi/info:eu-repo/semantics/altIdentifier/url/http://www.tandfonline.com/doi/abs/10.1080/02664763.2014.993370http://www.tandfonline.com/doi/abs/10.1080/02664763.2014.993370info:eu-repo/semantics/altIdentifier/issn/0266-4763info:eu-repo/semantics/altIdentifier/doi/10.1080/02664763.2014.993370info:eu-repo/semantics/altIdentifier/doi/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:56:19Zoai:ri.conicet.gov.ar:11336/5055instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 11:56:20.128CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Bayesian approach to the inverse problem in a light scattering application
title Bayesian approach to the inverse problem in a light scattering application
spellingShingle Bayesian approach to the inverse problem in a light scattering application
Otero, Fernando Agustín
Bayesian Estimation
Particle Size Distribution
Inverse Problem
Metropolis-Hastings
Static Light Scattering
title_short Bayesian approach to the inverse problem in a light scattering application
title_full Bayesian approach to the inverse problem in a light scattering application
title_fullStr Bayesian approach to the inverse problem in a light scattering application
title_full_unstemmed Bayesian approach to the inverse problem in a light scattering application
title_sort Bayesian approach to the inverse problem in a light scattering application
dc.creator.none.fl_str_mv Otero, Fernando Agustín
Barreto Orlande, Helcio R.
Frontini, Gloria Lia
Elicabe, Guillermo Enrique
author Otero, Fernando Agustín
author_facet Otero, Fernando Agustín
Barreto Orlande, Helcio R.
Frontini, Gloria Lia
Elicabe, Guillermo Enrique
author_role author
author2 Barreto Orlande, Helcio R.
Frontini, Gloria Lia
Elicabe, Guillermo Enrique
author2_role author
author
author
dc.subject.none.fl_str_mv Bayesian Estimation
Particle Size Distribution
Inverse Problem
Metropolis-Hastings
Static Light Scattering
topic Bayesian Estimation
Particle Size Distribution
Inverse Problem
Metropolis-Hastings
Static Light Scattering
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In this article, static light scattering (SLS) measurements are processed to estimate the particle size distribution of particle systems incorporating prior information obtained from an alternative experimental technique: scanning electron microscopy (SEM). For this purpose we propose two Bayesian schemes (one parametric and another non-parametric) to solve the stated light scattering problem and take advantage of the obtained results to summarize some features of the Bayesian approach within the context of inverse problems. The features presented in this article include the improvement of the results when some useful prior information from an alternative experiment is considered instead of a non-informative prior as it occurs in a deterministic maximum likelihood estimation. This improvement will be shown in terms of accuracy and precision in the corresponding results and also in terms of minimizing the effect of multiple minima by including significant information in the optimization. Both Bayesian schemes are implemented using Markov Chain Monte Carlo methods. They have been developed on the basis of the Metropolis–Hastings (MH) algorithm using Matlab® and are tested with the analysis of simulated and experimental examples of concentrated and semi-concentrated particles. In the simulated examples, SLS measurements were generated using a rigorous model, while the inversion stage was solved using an approximate model in both schemes and also using the rigorous model in the parametric scheme. Priors from SEM micrographs were also simulated and experimented, where the simulated ones were obtained using a Monte Carlo routine. In addition to the presentation of these features of the Bayesian approach, some other topics will be discussed, such as regularization and some implementation issues of the proposed schemes, among which we remark the selection of the parameters used in the MH algorithm.
Fil: Otero, Fernando Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Mar del Plata. Instituto de Investigación En Ciencia y Tecnología de Materiales (i); Argentina. Universidad Nacional de Mar del Plata. Facultad de Ingeniería; Argentina
Fil: Barreto Orlande, Helcio R.. Universidade Federal Do Rio de Janeiro. Inst A.luiz Coimbra de Pos-graduacao E Pesquisa de Engenharia; Brasil
Fil: Frontini, Gloria Lia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Mar del Plata. Instituto de Investigación En Ciencia y Tecnología de Materiales (i); Argentina. Universidad Nacional de Mar del Plata. Facultad de Ingeniería; Argentina
Fil: Elicabe, Guillermo Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Mar del Plata. Instituto de Investigación En Ciencia y Tecnología de Materiales (i); Argentina. Universidad Nacional de Mar del Plata. Facultad de Ingeniería; Argentina
description In this article, static light scattering (SLS) measurements are processed to estimate the particle size distribution of particle systems incorporating prior information obtained from an alternative experimental technique: scanning electron microscopy (SEM). For this purpose we propose two Bayesian schemes (one parametric and another non-parametric) to solve the stated light scattering problem and take advantage of the obtained results to summarize some features of the Bayesian approach within the context of inverse problems. The features presented in this article include the improvement of the results when some useful prior information from an alternative experiment is considered instead of a non-informative prior as it occurs in a deterministic maximum likelihood estimation. This improvement will be shown in terms of accuracy and precision in the corresponding results and also in terms of minimizing the effect of multiple minima by including significant information in the optimization. Both Bayesian schemes are implemented using Markov Chain Monte Carlo methods. They have been developed on the basis of the Metropolis–Hastings (MH) algorithm using Matlab® and are tested with the analysis of simulated and experimental examples of concentrated and semi-concentrated particles. In the simulated examples, SLS measurements were generated using a rigorous model, while the inversion stage was solved using an approximate model in both schemes and also using the rigorous model in the parametric scheme. Priors from SEM micrographs were also simulated and experimented, where the simulated ones were obtained using a Monte Carlo routine. In addition to the presentation of these features of the Bayesian approach, some other topics will be discussed, such as regularization and some implementation issues of the proposed schemes, among which we remark the selection of the parameters used in the MH algorithm.
publishDate 2014
dc.date.none.fl_str_mv 2014-11-25
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/5055
Otero, Fernando Agustín; Barreto Orlande, Helcio R.; Frontini, Gloria Lia; Elicabe, Guillermo Enrique; Bayesian approach to the inverse problem in a light scattering application; Routledge Journals, Taylor & Francis Ltd; Journal of Applied Statistics; 42; 5; 25-11-2014; 994-1016
0266-4763
url http://hdl.handle.net/11336/5055
identifier_str_mv Otero, Fernando Agustín; Barreto Orlande, Helcio R.; Frontini, Gloria Lia; Elicabe, Guillermo Enrique; Bayesian approach to the inverse problem in a light scattering application; Routledge Journals, Taylor & Francis Ltd; Journal of Applied Statistics; 42; 5; 25-11-2014; 994-1016
0266-4763
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/
info:eu-repo/semantics/altIdentifier/url/http://www.tandfonline.com/doi/abs/10.1080/02664763.2014.993370http://www.tandfonline.com/doi/abs/10.1080/02664763.2014.993370
info:eu-repo/semantics/altIdentifier/issn/0266-4763
info:eu-repo/semantics/altIdentifier/doi/10.1080/02664763.2014.993370
info:eu-repo/semantics/altIdentifier/doi/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Routledge Journals, Taylor & Francis Ltd
publisher.none.fl_str_mv Routledge Journals, Taylor & Francis Ltd
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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