Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Falco J. Bargagli Stoffi"'
Autor:
Sean Anthony Byrne, Adam Peter Frederick Reynolds, Carolina Biliotti, Falco J. Bargagli-Stoffi, Luca Polonio, Massimo Riccaboni
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-13 (2023)
Abstract Eye movement data has been extensively utilized by researchers interested in studying decision-making within the strategic setting of economic games. In this paper, we demonstrate that both deep learning and support vector machine classifica
Externí odkaz:
https://doaj.org/article/e46fa7b4972e44658a030b214543765a
Publikováno v:
Harvard Data Science Review (2021)
Externí odkaz:
https://doaj.org/article/f2bcc747798648008c03de3674f5a4c6
Publikováno v:
Minds and Machines. 32:13-42
The idea that “simplicity is a sign of truth”, and the related “Occam’s razor” principle, stating that, all other things being equal, simpler models should be preferred to more complex ones, have been long discussed in philosophy and scienc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::272783305ea907ec0efa7d872456f5f9
https://doi.org/10.1007/s11023-022-09592-z
https://doi.org/10.1007/s11023-022-09592-z
Exposure to unconventional oil and gas development and all-cause mortality in Medicare beneficiaries
Autor:
Longxiang Li, Francesca Dominici, Annelise J. Blomberg, Falco J. Bargagli-Stoffi, Joel D. Schwartz, Brent A. Coull, John D. Spengler, Yaguang Wei, Joy Lawrence, Petros Koutrakis
Publikováno v:
Nat Energy
Little is known about whether exposure to unconventional oil and gas development is associated with higher mortality risks in the elderly and whether related air pollutants are exposure pathways. We studied a cohort of 15,198,496 Medicare beneficiari
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dd33477327d6a03fc48e74d84e90b7af
https://doi.org/10.1038/s41560-021-00970-y
https://doi.org/10.1038/s41560-021-00970-y
Publikováno v:
Machine Learning, Optimization, and Data Science ISBN: 9783031258909
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f10cd8d2467371b67ae7ae81938f4dfd
https://doi.org/10.1007/978-3-031-25891-6_15
https://doi.org/10.1007/978-3-031-25891-6_15
Publikováno v:
Annals of Applied Statistics, 16(3), 1986-2009. Institute of Mathematical Statistics
This paper introduces an innovative Bayesian machine learning algorithm to draw interpretable inference on heterogeneous causal effects in the presence of imperfect compliance (e.g., under an irregular assignment mechanism). We show, through Monte Ca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5ee83ed40d0dc28a6c512ac1c5073135
https://cris.maastrichtuniversity.nl/en/publications/0874a363-50d9-4ee5-b8fb-233328848095
https://cris.maastrichtuniversity.nl/en/publications/0874a363-50d9-4ee5-b8fb-233328848095
Publikováno v:
ISEE Conference Abstracts. 2021
BACKGROUND AND AIM: In environmental health sciences, it is critically important to identify subgroups of the study population where a treatment (or exposure) has a notably larger or smaller causal...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::620d87f7dcfd11a367a6882bbec7dbc3
https://doi.org/10.1289/isee.2021.o-sy-108
https://doi.org/10.1289/isee.2021.o-sy-108
Publikováno v:
Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography. 34(6)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ae14a5125f6176b380f157211bdd0124
https://pubmed.ncbi.nlm.nih.gov/33508414
https://pubmed.ncbi.nlm.nih.gov/33508414