Lesson 22c Hierarchical Bayes: JAGS examples
Channel: Michael Dietze
Date: 2020-03-24

Boston University EE509 "Applied Environmental Statistics" Course: This lecture is the third in a series on Hierarchical Bayesian models and looks at how to implement simple random effects models in JAGS. We show that model selection can be used to choose among different random effects and that the estimates of random effect variability are useful for helping us prioritize where to focus our attention (i.e. which scales have the most unexplained variability)   http://people.bu.edu/dietze/Bayes2020/EE509.htm