Ensuring identifiability in hierarchical mixed effects Bayesian models
Abstract
Ecologists are increasingly familiar with Bayesian statistical modeling and its associated Markov chain Monte Carlo (MCMC) methodology to infer about or to discover interesting effects in data. The complexity of ecological data often suggests implementation of (statistical) models with a commensurately rich structure of effects, including crossed or nested (i.e., hierarchical or multi‐level) structures of fixed and/or random effects. Yet, our...
Paper Details
Title
Ensuring identifiability in hierarchical mixed effects Bayesian models
Published Date
Jun 15, 2020
Journal
Volume
30
Issue
7
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