Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Víctor Gallego"'
Autor:
Víctor Gallego Herrera, María de los Ángeles Blasco Rocamora, Dolores María Martínez Buendía, María del Carmen Cuines Rodríguez, Vanessa Roldan Barreiro
Publikováno v:
European Journal of Health Research, Vol 9, Iss 1, Pp 1-12 (2023)
El cáncer de mama constituye el tumor maligno diagnosticado con más frecuencia en mujeres. El tamoxifeno es la droga adyuvante de elección en el tratamiento endocrino del cáncer de mama con receptores estrogénicos positivos. Posee efectos que re
Externí odkaz:
https://doaj.org/article/a277b25fb9bc4df2ba3de5511944fa72
Autor:
Víctor Gallego Herrera, Nereida María Albaladejo Garrido, Vanessa Roldan Barreiro, María del Carmen Cuines Rodríguez, Ana Belén Gracia Velasco
Publikováno v:
European Journal of Health Research, Vol 9, Iss 1, Pp 1-14 (2023)
La contaminación acústica y el ruido presentan una relación directa con la salud. A pesar de que en España existen leyes que legislan el ruido destinadas a reducir los riesgos a su exposición entre los trabajadores, en general no existe concienc
Externí odkaz:
https://doaj.org/article/a6430deac1904fe9862eff5711185d12
Autor:
Víctor Gallego, David Ríos Insua
Publikováno v:
Entropy, Vol 23, Iss 1, p 123 (2021)
In this work, a framework to boost the efficiency of Bayesian inference in probabilistic models is introduced by embedding a Markov chain sampler within a variational posterior approximation. We call this framework “refined variational approximatio
Externí odkaz:
https://doaj.org/article/73f0661639154955a23effef7e82a53a
Publikováno v:
Journal of the American Statistical Association. :1-12
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5846948ea0f66dc9f353e2c49f5dc2f8
https://doi.org/10.1080/01621459.2023.2183129
https://doi.org/10.1080/01621459.2023.2183129
Autor:
Víctor Gallego, David Ríos Insua
Publikováno v:
Digital.CSIC. Repositorio Institucional del CSIC
instname
instname
This article reviews current advances and developments in neural networks. This requires recalling some of the earlier work in the field. We emphasize Bayesian approaches and their benefits compared to more standard maximum likelihood treatments. Sev
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::de869f5b16f72c86038486b1e07b7374
http://hdl.handle.net/10261/271445
http://hdl.handle.net/10261/271445
How do tuna schools associate to dFADs? A study using echo-sounder buoys to identify global patterns
Autor:
Manuel Navarro-García, Daniel Precioso, Kathryn Gavira-O’Neill, Alberto Torres-Barrán, David Gordo, Víctor Gallego, David Gómez-Ullate
Based on the data gathered by echo-sounder buoys attached to drifting Fish Aggregating Devices (dFADs) across tropical oceans, the current study applies a Machine Learning protocol to examine the temporal trends of tuna schools’ association to drif
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c8b10b14a58158f21dd2c1b8af5144e6
Assessing the effect of advertising expenditures upon sales: A Bayesian structural time series model
Publikováno v:
E-Prints Complutense. Archivo Institucional de la UCM
instname
E-Prints Complutense: Archivo Institucional de la UCM
Universidad Complutense de Madrid
instname
E-Prints Complutense: Archivo Institucional de la UCM
Universidad Complutense de Madrid
We propose a robust implementation of the Nerlove--Arrow model using a Bayesian structural time series model to explain the relationship between advertising expenditures of a country-wide fast-food franchise network with its weekly sales. Thanks to t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f89a77c0704013775c3f0eb4af11494b
https://doi.org/10.1002/asmb.2460
https://doi.org/10.1002/asmb.2460
Publikováno v:
Digital.CSIC. Repositorio Institucional del CSIC
instname
Molecular Diversity
Digital.CSIC: Repositorio Institucional del CSIC
Consejo Superior de Investigaciones Científicas (CSIC)
instname
Molecular Diversity
Digital.CSIC: Repositorio Institucional del CSIC
Consejo Superior de Investigaciones Científicas (CSIC)
19 p.-5 fig.-1 graph. abst.
The introduction of a new drug to the commercial market follows a complex and long process that typically spans over several years and entails large monetary costs due to a high attrition rate. Because of this, there
The introduction of a new drug to the commercial market follows a complex and long process that typically spans over several years and entails large monetary costs due to a high attrition rate. Because of this, there
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fcb81a45197e573151531a70d1b1cb43
http://hdl.handle.net/10261/245893
http://hdl.handle.net/10261/245893
Autor:
David Ríos Insua, Víctor Gallego
Publikováno v:
Entropy
Digital.CSIC. Repositorio Institucional del CSIC
instname
Volume 23
Issue 1
Entropy, Vol 23, Iss 123, p 123 (2021)
Digital.CSIC. Repositorio Institucional del CSIC
instname
Volume 23
Issue 1
Entropy, Vol 23, Iss 123, p 123 (2021)
In this work, a framework to boost the efficiency of Bayesian inference in probabilistic models is introduced by embedding a Markov chain sampler within a variational posterior approximation. We call this framework &ldquo
refined variational app
refined variational app
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::79db29b72760109736f1c6f430dc3728
https://pubmed.ncbi.nlm.nih.gov/33477766
https://pubmed.ncbi.nlm.nih.gov/33477766
Publikováno v:
Digital.CSIC. Repositorio Institucional del CSIC
instname
E-Prints Complutense. Archivo Institucional de la UCM
E-Prints Complutense: Archivo Institucional de la UCM
Universidad Complutense de Madrid
Mathematics
Volume 8
Issue 11
Mathematics, Vol 8, Iss 1957, p 1957 (2020)
instname
E-Prints Complutense. Archivo Institucional de la UCM
E-Prints Complutense: Archivo Institucional de la UCM
Universidad Complutense de Madrid
Mathematics
Volume 8
Issue 11
Mathematics, Vol 8, Iss 1957, p 1957 (2020)
Adversarial classification (AC) is a major subfield within the increasingly important domain of adversarial machine learning (AML). So far, most approaches to AC have followed a classical game-theoretic framework. This requires unrealistic common kno
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d89c1a05c203bc8276d842b93d536b28
https://eprints.ucm.es/id/eprint/63167/
https://eprints.ucm.es/id/eprint/63167/