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dc.contributor.author
Otero, Fernando Agustín  
dc.contributor.author
Barreto Orlande, Helcio R.  
dc.contributor.author
Frontini, Gloria Lia  
dc.contributor.author
Elicabe, Guillermo Enrique  
dc.date.available
2016-04-07T15:33:50Z  
dc.date.issued
2014-11-25  
dc.identifier.citation
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  
dc.identifier.issn
0266-4763  
dc.identifier.uri
http://hdl.handle.net/11336/5055  
dc.description.abstract
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.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Routledge Journals, Taylor & Francis Ltd  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
Bayesian Estimation  
dc.subject
Particle Size Distribution  
dc.subject
Inverse Problem  
dc.subject
Metropolis-Hastings  
dc.subject
Static Light Scattering  
dc.subject.classification
Estadística y Probabilidad  
dc.subject.classification
Matemáticas  
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS  
dc.title
Bayesian approach to the inverse problem in a light scattering application  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2016-05-06 15:52:43.262787-03  
dc.journal.volume
42  
dc.journal.number
5  
dc.journal.pagination
994-1016  
dc.journal.pais
Reino Unido  
dc.journal.ciudad
Londres  
dc.description.fil
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  
dc.description.fil
Fil: Barreto Orlande, Helcio R.. Universidade Federal Do Rio de Janeiro. Inst A.luiz Coimbra de Pos-graduacao E Pesquisa de Engenharia; Brasil  
dc.description.fil
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  
dc.description.fil
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  
dc.journal.title
Journal of Applied Statistics  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/10.1080/02664763.2014.993370  
dc.relation.alternativeid
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  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/issn/0266-4763  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1080/02664763.2014.993370  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://www.tandfonline.com/doi/abs/10.1080/02664763.2014.993370