7 papers
Making Recursive Bayesian Inference Robust
Myungsoo Yoo, Daniel Würzler Barreto, Mevin B. Hooten
While Bayesian inference has become increasingly popular with advances in computational resources, its algorithms can be computationally prohibitive and may not scale with large da…
Network knockoffs: controlling false discovery in dyadic space
Justin Van Ee, Yoichiro Kanno, Jacob Rash +1
Phenomena such as epidemiological processes, hydrologic systems, social platforms, utility services, and supply chains can be represented as topological networks. A central questio…
A multi-stage Bayesian approach to fit spatial point process models
Rachael Ren, Mevin B. Hooten, Toryn L. J. Schafer +4
Spatial point process (SPP) models are commonly used to analyze point pattern data in many fields, including presence-only data in ecology. Existing exact Bayesian methods for fitt…
Dyadic Flow Models for Nonstationary Gene Flow in Landscape Genomics
Michael R. Schwob, Nicholas M. Calzada, Justin J. Van Ee +6
The field of landscape genomics aims to infer how landscape features affect gene flow across space. Most landscape genomic frameworks assume the isolation-by-distance and isolation…
Recursive Adaptive Importance Sampling with Optimal Replenishment
Daniel Würzler Barreto, Mevin B. Hooten
Increased access to computing resources has led to the development of algorithms that can run efficiently on multi-core processing units or in distributed computing environments. I…
Spatial Hyperspheric Models for Compositional Data
Michael R. Schwob, Mevin B. Hooten, Nicholas M. Calzada +1
Compositional observations are an increasingly prevalent data source in spatial statistics. Analysis of such data is typically done on log-ratio transformations or via Dirichlet re…