Scripts and tutorials accompanying the COIN series of papers illustrating the use of Generalized Linear Models (GLMs) for astronomical data analysis.
These references showcase practical examples for Logistic, Gamma, Poisson, and Negative Binomial regressions applied to real astrophysical problems.
🧠 Part of the COIN Toolbox: advancing community-driven methods in astrostatistics.
References
🎓 Tutorial: Logistic Regression Guide
References
🎓 Tutorial: Gamma Regression Guide
References
-
The overlooked potential of generalized linear models in astronomy - III. Bayesian negative binomial regression and globular cluster populations
🎓 Code Snippet: Count Data (JAGS) -
Modeling Globular Cluster Counts with Bayesian Latent Models
🎓 Code Snippet: Negative Binomial Model (Nimble)
- de Souza, R. S., et al. (2021) Probabilistic Modeling of Asteroid Diameters from Gaia DR2 Errors (logistic Bayesian additive regression tree model)
- Emílio Zanatta et al. (2024) NSCs from groups to clusters: a catalogue of dwarf galaxies in the Shapley supercluster and the role of environment in galaxy nucleation (Logistic Regression)
- Hattab, M. W., de Souza, R. S., Ciardi, B. (2019). A Case Study of Hurdle and Generalized Additive Models in Astronomy: the Escape of Ionizing Radiation (Uses hurdle / GAM hybrid modeling)
- Dantas, Coelho, de Souza, & Gonçalves (2020). UV bright red-sequence galaxies: how do UV upturn systems evolve in redshift and stellar mass? (Applies Bayesian logistic model among other stats methods)
- de Souza, R. S., et al. (2016). Is the cluster environment quenching the Seyfert activity in elliptical and spiral galaxies? (Bayesian logistic regression in galaxy environment context)
The Cosmostatistics Initiative (COIN) is a global collaboration focused on developing and disseminating statistical and machine-learning techniques in astronomy.
Learn more at @COIN.