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Activity Number: 216 - Modern Bayesian Computing in Ecology
Type: Invited
Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
Sponsor: ENAR
Abstract #326509
Title: Hierarchical Computing for Hierarchical Models in Ecology
Author(s): Mevin Hooten*
Companies: Colorado State University
Keywords: Bayesian; trajectory; population; environmental; MCMC

With advances in data collection capabilities, ecological models are becoming increasingly sophisticated. For example, in studies involving remote observation of individual animals, there is a need for population-level inference. Hierarchical models naturally allow the researcher to scale up the inference. However, most efforts to scale up the inference to the population level are either post hoc or complicated enough that only the developer can implement the model. Deep hierarchical Bayesian models can be challenging to fit due to computational limitations or extensive tuning required. We propose a two-stage procedure for fitting hierarchical ecological models to data. The two-stage approach is statistically rigorous and allows one to fit individual-level models separately, then resample them using a secondary MCMC algorithm. The primary advantages of the two-stage approach are that the first stage is easily parallelizable and the second stage is completely unsupervised, allowing for an automated fitting procedure in many cases. We demonstrate the two-stage procedure with two applications of animal movement models.

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