2 citations · 2 across the 3 of their papers we have counts for
10 papers
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…
Animal Movement Models with Mechanistic Selection Functions
Mevin B. Hooten, Xinyi Lu, Martha J. Garlick +1
A suite of statistical methods are used to study animal movement. Most of these methods treat animal telemetry data in one of three ways: as discrete processes, as continuous proce…
Hierarchical approaches for flexible and interpretable binary regression models
Henry R. Scharf, Xinyi Lu, Perry J. Williams +1
Binary regression models are ubiquitous in virtually every scientific field. Frequently, traditional generalized linear models fail to capture the variability in the probability su…
Predicting paleoclimate from compositional data using multivariate Gaussian process inverse prediction
John R. Tipton, Mevin B. Hooten, Connor Nolan +2
Multivariate compositional count data arise in many applications including ecology, microbiology, genetics, and paleoclimate. A frequent question in the analysis of multivariate co…
Model Selection using Multi-Objective Optimization
Perry Williams, William Kendall, Mevin Hooten
Choices in scientific research and management require balancing multiple, often competing objectives.Multiple-objective optimization (MOO) provides a unifying framework for solving…
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…