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Bayesian spatial analysis of hardwood tree counts in forests via MCMC
Author(s) -
Entezari Reihaneh,
Brown Patrick E.,
Rosenthal Jeffrey S.
Publication year - 2020
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.2608
Subject(s) - markov chain monte carlo , bayesian probability , stratified sampling , statistics , sampling (signal processing) , computer science , spatial analysis , logistic regression , sample (material) , mathematics , forestry , geography , chemistry , filter (signal processing) , chromatography , computer vision
In this paper, we use a Bayesian spatial model to spatially interpolate forest inventory 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 Bayesian 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.