Bayesian Spatial Analysis of Hardwood Tree Counts in Forests via MCMC
Applications
2018-07-04 v1 Computation
Methodology
Abstract
In this paper, we perform Bayesian Inference to analyze spatial tree count data from the Timiskaming and Abitibi River forests in Ontario, Canada. We consider a Bayesian Generalized Linear Geostatistical Model and implement a Markov Chain Monte Carlo algorithm to sample from its posterior distribution. How spatial predictions for new sites in the forests change as the amount of training data is reduced is studied and compared with a Logistic Regression model without a spatial effect. Finally, we discuss a stratified sampling approach for selecting subsets of data that allows for potential better predictions.
Keywords
Cite
@article{arxiv.1807.01239,
title = {Bayesian Spatial Analysis of Hardwood Tree Counts in Forests via MCMC},
author = {Reihaneh Entezari and Patrick E. Brown and Jeffrey S. Rosenthal},
journal= {arXiv preprint arXiv:1807.01239},
year = {2018}
}