A Tutorial on Bayesian Analysis of Count Data Using JAGS

Authors

  • Sijing Shao * University of Notre Dame

DOI:

https://doi.org/10.35566/jbds/v2n2/shao

Keywords:

Count data, Zero-inflation, Poisson regression, ZIP model, Hurdle model

Abstract

In behavioral studies, the frequency of a particular behavior or event is often collected and the acquired data are referred to as count data. This tutorial introduces readers to Poisson regression models which is a more appropriate approach for such data. Meanwhile, count data with excessive zeros often occur in behavioral studies and models such as zero-inflated or hurdle models can be employed for handling zero-inflation in the count data. In this tutorial, we aim to cover the necessary fundamentals for these methods and equip readers with application tools of JAGS. Examples of the implementation of the models in JAGS from within R are provided for demonstration purposes.

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Published

2022-12-14

How to Cite

Shao, S. (2022). A Tutorial on Bayesian Analysis of Count Data Using JAGS. Journal of Behavioral Data Science, 2(2), 156–173. https://doi.org/10.35566/jbds/v2n2/shao