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: | , , |
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| বিন্যাস: | Online |
| ভাষা: | ইংরেজি |
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InTechOpen
2021
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| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | ONIX_20210602_10.5772/intechopen.92159_471 |
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| _version_ | 1869525182199103488 |
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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. |
| format | Online |
| id | doab-20.500.12854ir-70219 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | InTechOpen |
| publisherStr | InTechOpen |
| record_format | ojs |
| 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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