39 citations · 106 across the 5 of their papers we have counts for
5 papers
Using gamma regression for photometric redshifts of survey galaxies
J. Elliott, R. S. de Souza, A. Krone-Martins +3
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 alte…
The Overlooked Potential of Generalized Linear Models in Astronomy-III: Bayesian Negative Binomial Regression and Globular Cluster Populations
R. S. de Souza, J. M. Hilbe, B. Buelens +5
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 whic…
The Overlooked Potential of Generalized Linear Models in Astronomy-II: Gamma regression and photometric redshifts
J. Elliott, R. S. de Souza, A. Krone-Martins +3
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 abo…
The Overlooked Potential of Generalized Linear Models in Astronomy - I: Binomial Regression
R. S. de Souza, E. Cameron, M. Killedar +6
Revealing hidden patterns in astronomical data is often the path to fundamental scientific breakthroughs; meanwhile the complexity of scientific inquiry increases as more subtle re…
New Organizations to Support Astroinformatics and Astrostatistics
Eric D. Feigelson, Željko Ivezić, Joseph Hilbe +1
In the past two years, the environment within which astronomers conduct their data analysis and management has rapidly changed. Working Groups associated with international societi…