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stat.ME2020
Greater Than the Sum of its Parts: Computationally Flexible Bayesian Hierarchical Modeling
Devin S. Johnson, Brian M. Brost, Mevin B. Hooten
We propose a multistage method for making inference at all levels of a Bayesian hierarchical model (BHM) using natural data partitions to increase efficiency by allowing computatio…
stat.ME2018
Making Recursive Bayesian Inference Accessible
Mevin B. Hooten, Devin S. Johnson, Brian M. Brost
Bayesian models provide recursive inference naturally because they can formally reconcile new data and existing scientific information. However, popular use of Bayesian methods oft…
stat.ME2016
Hierarchical animal movement models for population-level inference
Mevin B. Hooten, Frances E. Buderman, Brian M. Brost +2
New methods for modeling animal movement based on telemetry data are developed regularly. With advances in telemetry capabilities, animal movement models are becoming increasingly…