5 citations · 17 across the 14 of their papers we have counts for
6 papers · 1 filter
Bayesian Modeling of Incompatible Spatial Data: A Case Study Involving Post-Adrian Storm Forest Damage Assessment
Lu Zhang, Andrew O. Finley, Arne Nothdurft +1
Modeling incompatible spatial data, i.e., data with different spatial resolutions, is a pervasive challenge in remote sensing data analysis. Typical approaches to addressing this c…
Bayesian Hierarchical Modeling and Inference for Mechanistic Systems in Industrial Hygiene
Soumyakanti Pan, Darpan Das, Gurumurthy Ramachandran +1
A series of experiments in stationary and moving passenger rail cars were conducted to measure removal rates of particles in the size ranges of SARS-CoV-2 viral aerosols, and the a…
Finite Population Survey Sampling: An Unapologetic Bayesian Perspective
Sudipto Banerjee
This article attempts to offer some perspectives on Bayesian inference for finite population quantities when the units in the population are assumed to exhibit complex dependencies…
Bayesian Geostatistics Using Predictive Stacking
Lu Zhang, Wenpin Tang, Sudipto Banerjee
We develop Bayesian predictive stacking for geostatistical models, where the primary inferential objective is to provide inference on the latent spatial random field and conduct sp…
Bayesian modeling with spatial curvature processes
Aritra Halder, Sudipto Banerjee, Dipak K. Dey
Spatial process models are widely used for modeling point-referenced variables arising from diverse scientific domains. Analyzing the resulting random surface provides deeper insig…
Models to support forest inventory and small area estimation using sparsely sampled LiDAR: A case study involving G-LiHT LiDAR in Tanana, Alaska
Andrew O. Finley, Hans-Erik Andersen, Chad Babcock +3
A two-stage hierarchical Bayesian model is developed and implemented to estimate forest biomass density and total given sparsely sampled LiDAR and georeferenced forest inventory pl…