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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/5055
Ver los metadatos del registro completo
id |
CONICETDig_86b626f52d301158915231260f78f50b |
---|---|
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 Técnicas2025-09-29T10:23:31Zoai: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:34982025-09-29 10:23:31.745CONICET 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 |
_version_ |
1844614230582493184 |
score |
13.070432 |