Elements of hierarchical Bayesian inference

Abstract

Abstract Serious investigation of ecological processes is challenging due to the complex nature of these processes and the lack of sufficient data to see them well. Hence, acknowledging our limitations, we turn to stochastic modeling as a means to capture the uncertainty in inference about the process. Since typically, such processes involve components at different levels, stages, and scales, it is natural to frame our modeling in the context of hierarchical models. In turn, since such models introduce unknowns, for example, parameters or latent processes, we need to incorporate the uncertainty associated with these unknowns in order to achieve a better overall assessment of uncertainty.