Bayesian Analysis of Generalized Linear Models for Count Data using Stan
Keywords:
Bayesian inference, Poisson regression, negative binomial regression, posterior, Stan, LOOIC, WAICAbstract
In this paper, Bayesian approach is used to model count data for generalized linear models (GLMs) using Stan language. Commonly used GLMs include logistic regression for binary data and Poisson regression or Negative binomial regression for count data. The loo package is used for model selection. The Bayesian model comparison criteria LOOIC and WAIC are applied to evaluate the models. Furthermore, parallel simulations tools are also implemented with an extensive use of R.
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