A Bayesian framework to update scaling factors for radioactive waste characterization

Biagio Zaffora; Séverine Demeyer; Matteo Magistris; Elvezio Ronchetti; Gilbert Saporta; Chris Theis

doi:10.1016/j.apradiso.2020.109092

Journal Articles Applied Radiation and Isotopes Year : 2020

A Bayesian framework to update scaling factors for radioactive waste characterization

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Biagio Zaffora

Function : Author

CERN [Genève]

Séverine Demeyer

Function : Author

Laboratoire National de Métrologie et d'Essais [Trappes]

Matteo Magistris

Function : Author

CERN [Genève]

Elvezio Ronchetti

Function : Author

Université de Genève = University of Geneva

Gilbert Saporta

Function : Author
PersonId : 180161
IdHAL : gilbert-saporta
ORCID : 0000-0002-3406-5887
IdRef : 027122565

CEDRIC. Méthodes statistiques de data-mining et apprentissage

CV

Chris Theis

Function : Author

CERN [Genève]

Domains

Nuclear Experiment [nucl-ex] Statistics [stat]

Gilbert Saporta : Connect in order to contact the contributor

https://cnam.hal.science/hal-02491880

Submitted on : Wednesday, February 26, 2020-2:31:26 PM

Last modification on : Friday, July 19, 2024-11:38:05 AM

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hal-02491880 , version 1 (26-02-2020)

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HAL Id : hal-02491880 , version 1
DOI : 10.1016/j.apradiso.2020.109092

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Biagio Zaffora, Séverine Demeyer, Matteo Magistris, Elvezio Ronchetti, Gilbert Saporta, et al.. A Bayesian framework to update scaling factors for radioactive waste characterization. Applied Radiation and Isotopes, 2020, pp.109092. ⟨10.1016/j.apradiso.2020.109092⟩. ⟨hal-02491880⟩

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A Bayesian framework to update scaling factors for radioactive waste characterization

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