Chapter Bayesian Approach for X-Ray and Neutron Scattering Spectroscopy

The rapidly improving performance of inelastic scattering instruments has prompted tremendous advances in our knowledge of the high-frequency dynamics of disordered systems, yet also imposing new demands to the data analysis and interpretation. This ongoing effort is likely to reach soon an impasse,...

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প্রধান লেখক: Cunsolo, Alessandro, Scaccia, Luisa, De Francesco, Alessio
বিন্যাস: Online
ভাষা:ইংরেজি
প্রকাশিত: InTechOpen 2021
বিষয়গুলি:
অনলাইন ব্যবহার করুন:ONIX_20210602_10.5772/intechopen.92159_471
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author Cunsolo, Alessandro
Scaccia, Luisa
De Francesco, Alessio
author_browse Cunsolo, Alessandro
De Francesco, Alessio
Scaccia, Luisa
author_facet Cunsolo, Alessandro
Scaccia, Luisa
De Francesco, Alessio
author_sort Cunsolo, Alessandro
collection Directory of Open Access Books
description The rapidly improving performance of inelastic scattering instruments has prompted tremendous advances in our knowledge of the high-frequency dynamics of disordered systems, yet also imposing new demands to the data analysis and interpretation. This ongoing effort is likely to reach soon an impasse, unless new protocols are developed in the data modeling. This need stems from the increasingly detailed information sought for in typical line shape measurements, which often touches or crosses the boundaries imposed by the limited experimental accuracy. Given this scenario, the risk of a bias and an over-parametrized data modeling represents a concrete threat for further advances in the field. Being aware of the severity of the problem, we illustrate here the new hopes brought in this area by Bayesian inference methods. Making reference to recent literature results, we demonstrate the superior ability of these methods in providing a probabilistic and evidence-based modeling of experimental data. Most importantly, this approach can enable hypothesis test involving competitive line shape models and is intrinsically equipped with natural antidotes against the risk of over-parametrization as it naturally enforces the Occam maximum parsimony principle, which favors intrinsically simple models over overly complex ones.
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spelling doab-20.500.12854ir-702192025-08-13T14:11:41Z Chapter Bayesian Approach for X-Ray and Neutron Scattering Spectroscopy Cunsolo, Alessandro Scaccia, Luisa De Francesco, Alessio inelastic X-ray scattering, inelastic neutron scattering, Bayes analysis, MCMC methods, model choice thema EDItEUR::P Mathematics and Science::PH Physics thema EDItEUR::P Mathematics and Science::PH Physics The rapidly improving performance of inelastic scattering instruments has prompted tremendous advances in our knowledge of the high-frequency dynamics of disordered systems, yet also imposing new demands to the data analysis and interpretation. This ongoing effort is likely to reach soon an impasse, unless new protocols are developed in the data modeling. This need stems from the increasingly detailed information sought for in typical line shape measurements, which often touches or crosses the boundaries imposed by the limited experimental accuracy. Given this scenario, the risk of a bias and an over-parametrized data modeling represents a concrete threat for further advances in the field. Being aware of the severity of the problem, we illustrate here the new hopes brought in this area by Bayesian inference methods. Making reference to recent literature results, we demonstrate the superior ability of these methods in providing a probabilistic and evidence-based modeling of experimental data. Most importantly, this approach can enable hypothesis test involving competitive line shape models and is intrinsically equipped with natural antidotes against the risk of over-parametrization as it naturally enforces the Occam maximum parsimony principle, which favors intrinsically simple models over overly complex ones. 2021-06-02T10:13:09Z 2020 chapter ONIX_20210602_10.5772/intechopen.92159_471 https://library.oapen.org/handle/20.500.12657/49357 https://directory.doabooks.org/handle/20.500.12854/70219 eng open access image/jpeg image/jpeg image/jpeg n/a n/a n/a https://library.oapen.org/bitstream/20.500.12657/49357/1/72339.pdf https://library.oapen.org/bitstream/20.500.12657/49357/1/72339.pdf https://library.oapen.org/bitstream/20.500.12657/49357/1/72339.pdf InTechOpen 10.5772/intechopen.92159 10.5772/intechopen.92159 035ecc65-6737-43cf-a13a-6bdf67ce01f4 open access
spellingShingle inelastic X-ray scattering, inelastic neutron scattering, Bayes analysis, MCMC methods, model choice
thema EDItEUR::P Mathematics and Science::PH Physics
thema EDItEUR::P Mathematics and Science::PH Physics
Cunsolo, Alessandro
Scaccia, Luisa
De Francesco, Alessio
Chapter Bayesian Approach for X-Ray and Neutron Scattering Spectroscopy
title Chapter Bayesian Approach for X-Ray and Neutron Scattering Spectroscopy
title_full Chapter Bayesian Approach for X-Ray and Neutron Scattering Spectroscopy
title_fullStr Chapter Bayesian Approach for X-Ray and Neutron Scattering Spectroscopy
title_full_unstemmed Chapter Bayesian Approach for X-Ray and Neutron Scattering Spectroscopy
title_short Chapter Bayesian Approach for X-Ray and Neutron Scattering Spectroscopy
title_sort chapter bayesian approach for x ray and neutron scattering spectroscopy
topic inelastic X-ray scattering, inelastic neutron scattering, Bayes analysis, MCMC methods, model choice
thema EDItEUR::P Mathematics and Science::PH Physics
thema EDItEUR::P Mathematics and Science::PH Physics
topic_facet inelastic X-ray scattering, inelastic neutron scattering, Bayes analysis, MCMC methods, model choice
thema EDItEUR::P Mathematics and Science::PH Physics
thema EDItEUR::P Mathematics and Science::PH Physics
url ONIX_20210602_10.5772/intechopen.92159_471
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