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Bayes in action in deep learning and dictionary learning.

• Authors : Arbel, Julyan¹; Dang, Hong-Phuong²; Elvira, Clement³

• Source: ESAIM: Proceedings & Surveys. 2023, Vol. 74, p90-107. 18p.

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Modelling of failure rate of water supply network using the Bayes theorem

• Authors : Szpak Dawid; Tchórzewska-Cieślak Barbara

Subjects: Environmental sciences; GE1-350

• Source: E3S Web of Conferences, Vol 44, p 00175 (2018)

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Inferring turbulent environments via machine learning.

• Authors : Buzzicotti M; Department of Physics and INFN, University of Rome 'Tor Vergata', Via della Ricerca Scientifica 1, 00133, Rome, Italy. .; Bonaccorso F

Subjects: Machine Learning* ; Neural Networks, Computer*; Bayes Theorem

• Source: The European physical journal. E, Soft matter [Eur Phys J E Soft Matter] 2022 Dec 31; Vol. 45 (12), pp. 102. Date of Electronic Publication: 2022 Dec 31.Publisher: EDP Sciences Country of Publication: France NLM ID: 101126530 Publication Model: Electronic Cited Medium:

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Trends on Major Actinides from an Integral Data Assimilation.

• Authors : Rimpault, Gerald; Huy, Virginie; Noguère, Gilles

Subjects: ACTINIDE elements; NUCLEAR physics experiments; URANIUM isotopes

• Source: EPJ Web of Conferences; 2019, Vol. 211, pN.PAG-N.PAG, 8p

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To be(t) or not to be(t): A Bayesian approach to statistical data analysis.

• Authors : Pisano, Silvia^1,2 (AUTHOR)

• Source: EPJ Web of Conferences. 7/11/2025, Vol. 331, p1-9. 9p.

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Stochastic approach for radionuclides quantification

• Authors : Clement A.; Saurel N.; Perrin G.

Subjects: Nuclear quantification; Bayes Theorem; MCMC

• Source: EPJ Web of Conferences, Vol 170, p 06002 (2018)

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Comparative study of non-invasive force and stress inference methods in tissue.

• Authors : Ishihara S; Graduate School of Arts and Sciences, The University of Tokyo, Komaba 3-8-1, Meguro-ku, Tokyo, Japan. ; Sugimura K

Subjects: Models, Biological* ; Stress, Mechanical*; Drosophila melanogaster/Drosophila melanogaster/Drosophila melanogaster/*cytology

• Source: The European physical journal. E, Soft matter [Eur Phys J E Soft Matter] 2013 Apr; Vol. 36 (4), pp. 9859. Date of Electronic Publication: 2013 Apr 26.Publisher: EDP Sciences Country of Publication: France NLM ID: 101126530 Publication Model: Print-Electronic Cited Medium:

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Utilizing prior knowledge about the measurement process for uncertainty evaluation through plain Monte Carlo sampling.

• Authors : Marschall, Manuel¹ (AUTHOR) ; Demeyer, Séverine² (AUTHOR); Petit, Sébastien² (AUTHOR)

• Source: International Journal of Metrology & Quality Engineering. 2024, Vol. 15, p1-17. 17p.

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Bayesian Monte Carlo Evaluation of Imperfect (n, 233U) Data and Model.

• Authors : Brown, Jesse M.¹ ; Arbanas, Goran¹ ; Wiarda, Dorothea¹

• Source: EPJ Web of Conferences. 5/26/2023, Vol. 284, p1-4. 4p.

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Bayesian Monte Carlo Evaluation of Imperfect (n, 233U) Data and Model.

• Authors : Brown, Jesse M.; Arbanas, Goran; Wiarda, Dorothea

Subjects: URANIUM isotopes; NEUTRON cross sections; MONTE Carlo method

• Source: EPJ Web of Conferences; 5/26/2023, Vol. 284, p1-4, 4p

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