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.

中文翻译：

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通过 MCMC 对森林中阔叶树数量的贝叶斯空间分析

在本文中，我们执行贝叶斯推理来分析来自加拿大安大略省 Timiskaming 和 Abitibi 河森林的空间树木计数数据。我们考虑贝叶斯广义线性地统计模型并实现马尔可夫链蒙特卡罗算法以从其后验分布中采样。研究了森林中新站点的空间预测如何随着训练数据量的减少而变化，并与没有空间效应的逻辑回归模型进行比较。最后，我们讨论了一种用于选择数据子集的分层抽样方法，以实现潜在的更好预测。