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pro vyhledávání: '"Gustavo Z Dos Santos Lima"'
Autor:
Gustavo Z Dos Santos Lima, João V T de Lima, João M de Araújo, Gilberto Corso, Sérgio Luiz E F da Silva
Publikováno v:
PLoS ONE, Vol 18, Iss 3, p e0282578 (2023)
The conventional approach to data-driven inversion framework is based on Gaussian statistics that presents serious difficulties, especially in the presence of outliers in the measurements. In this work, we present maximum likelihood estimators associ
Externí odkaz:
https://doaj.org/article/f7c410d8aae34d588a70831286d171e0
Autor:
Gustavo Z. dos Santos Lima, João V. T. de Lima, João M. de Araújo, Gilberto Corso, Sérgio Luiz E. F. da Silva
The conventional approach to data-driven inversion framework is based on Gaussian statistics that presents serious difficulties, especially in the presence of outliers in the measurements. In this work, we present maximum likelihood estimators associ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::92e2f80df01112089a377be36b213665
Autor:
Sérgio Luiz E.F. da Silva, R. Silva, Gustavo Z. dos Santos Lima, João M. de Araújo, Gilberto Corso
Publikováno v:
Physica A: Statistical Mechanics and its Applications. 600:127554
In this work we propose a robust methodology to mitigate the undesirable effects caused by outliers to generate reliable physical models. In this way, we formulate the inverse problems theory in the context of Kaniadakis statistical mechanics (or $\k
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d943a9ecf19a52620aca9b939d18f634
https://doi.org/10.1016/j.physa.2022.127554
https://doi.org/10.1016/j.physa.2022.127554
Autor:
dos Santos Lima, Gustavo Z.1 (NURSE) guzampier76@gmail.com, de Lima, João V. T.2 (NURSE), de Araújo, João M.2 (NURSE), Corso, Gilberto3 (NURSE), da Silva, Sérgio Luiz E. F.4,5 (AUTHOR)
Publikováno v:
PLoS ONE. 3/30/2023, Vol. 17 Issue 3, p1-22. 22p.
Autor:
Mahmoud Iravani, Susan Duty
Translational Approaches to Non-Motor Symptoms of Neurodegenerative Diseases, Volume 173 in the International Review on Neurobiology series, highlights new advances in the field with this new volume presenting interesting chapters on topics such as A