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Bayesian spatial analysis of hardwood tree counts in forests via MCMC
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.
Bayesian spatial analysis of hardwood tree counts in forests via MCMC
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.
Bayesian spatial analysis of hardwood tree counts in forests via MCMC
Entezari, Reihaneh (author) / Brown, Patrick E. (author) / Rosenthal, Jeffrey S. (author)
Environmetrics ; 31
2020-06-01
17 pages
Article (Journal)
Electronic Resource
English
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