Zobrazeno 1 - 10
of 91
pro vyhledávání: '"Hilbe, J."'
Autor:
de Souza, R. S., Dantas, M. L. L., Krone-Martins, A., Cameron, E., Coelho, P., Hattab, M. W., de Val-Borro, M., Hilbe, J. M., Elliott, J., Hagen, A.
Publikováno v:
MNRAS, 2016
We developed a hierarchical Bayesian model (HBM) to investigate how the presence of Seyfert activity relates to their environment, herein represented by the galaxy cluster mass, $M_{200}$, and the normalized cluster-centric distance, $r/r_{200}$. We
Externí odkaz:
http://arxiv.org/abs/1603.06256
Machine learning techniques offer a plethora of opportunities in tackling big data within the astronomical community. We present the set of Generalized Linear Models as a fast alternative for determining photometric redshifts of galaxies, a set of to
Externí odkaz:
http://arxiv.org/abs/1507.01293
Autor:
de Souza, R. S., Hilbe, J. M., Buelens, B., Riggs, J. D., Cameron, E., Ishida, E. E. O., Chies-Santos, A. L., Killedar, M.
In this paper, the third in a series illustrating the power of generalized linear models (GLMs) for the astronomical community, we elucidate the potential of the class of GLMs which handles count data. The size of a galaxy's globular cluster populati
Externí odkaz:
http://arxiv.org/abs/1506.04792
Autor:
de Souza, R. S., Cameron, E., Killedar, M., Hilbe, J., Vilalta, R., Maio, U., Biffi, V., Ciardi, B., Riggs, J. D.
Revealing hidden patterns in astronomical data is often the path to fundamental scientific breakthroughs; meanwhile the complexity of scientific inquiry increases as more subtle relationships are sought. Contemporary data analysis problems often elud
Externí odkaz:
http://arxiv.org/abs/1409.7696
Publikováno v:
Astronomy and Computing, Volume 10, April 2015, Pages 61-72
Machine learning techniques offer a precious tool box for use within astronomy to solve problems involving so-called big data. They provide a means to make accurate predictions about a particular system without prior knowledge of the underlying physi
Externí odkaz:
http://arxiv.org/abs/1409.7699
Autor:
de Souza, R.S., Cameron, E., Killedar, M., Hilbe, J., Vilalta, R., Maio, U., Biffi, V., Ciardi, B., Riggs, J.D.
Publikováno v:
In Astronomy and Computing September 2015 12:21-32
Publikováno v:
In Astronomy and Computing April 2015 10:61-72
Publikováno v:
NASA Astrophysics Data System
Machine learning techniques offer a plethora of opportunities in tackling big data within the astronomical community. We present the set of Generalized Linear Models as a fast alternative for determining photometric redshifts of galaxies, a set of to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7be5373fe81499a4d9c1a0b7e602bf73
http://arxiv.org/abs/1507.01293
http://arxiv.org/abs/1507.01293
Publikováno v:
A Beginner's Guide to GLM and GLMM with R.