Poisson/gamma random field models for spatial statistics
Poisson/gamma random field models for spatial statistics
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DOI：
10.1093/biomet/85.2.251
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发表时间：
1998-06-01
期刊：
影响因子：
2.7
通讯作者：
Ickstadt, K
中科院分区：
文献类型：
--
作者：
Wolpert, RL;Ickstadt, K
双随机贝叶斯分层模型的引入，以考虑：不确定性和空间变化的基本强度测量点过程模型。非齐次伽马过程随机场，更一般地说，马尔可夫随机场与无限可分的分布被用来构建正自相关的强度措施的空间泊松点过程，这些反过来又被用来模拟个别事件的数量和位置。一个数据扩充方案和马尔可夫链蒙特卡罗数值方法，生成贝叶斯后验和预测分布的样本。该方法是在连续和离散的设置，并适用于森林生态学的问题。
Doubly stochastic Bayesian hierarchical models are introduced to account for:uncertainty and spatial variation in the underlying intensity measure for-point process models. Inhomogeneous gamma process random fields and, more generally, Markov random fields with infinitely divisible distributions are used to construct positively autocorrelated intensity measures for spatial Poisson point processes; these in turn are used; to model the number and location of individual events. A data augmentation scheme and Markov chain Monte Carlo numerical methods are employed to generate,samples from Bayesian posterior and predictive distributions. The methods are developed in both continuous and discrete settings, and are applied to a problem in forest ecology.