Non‐parametric Bayesian Estimation of a Spatial Poisson Intensity
Non‐parametric Bayesian Estimation of a Spatial Poisson Intensity
复制标题
空间泊松强度的非参数贝叶斯估计
DOI：
10.1111/1467-9469.00114
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发表时间：
1998
影响因子：
1
通讯作者：
E. Arjas
中科院分区：
文献类型：
--
作者：
J. Heikkinen;E. Arjas
由Arjas & Gasbarra（1994）引入并随后由Arjas & Heikkinen（1997）修改的用于真实的线上强度的非参数贝叶斯估计的方法被推广到覆盖空间过程。该方法是基于一个模型近似的近似强度具有分段常数函数的结构。平面上的随机阶跃函数使用随机点图案的Voronoi镶嵌来生成。附近的强度值之间的平滑是通过马尔可夫随机场先验贝叶斯图像分析的精神。用真实的和模拟数据的例子说明了该方法的性能。
A method introduced by Arjas & Gasbarra (1994) and later modified by Arjas & Heikkinen (1997) for the non‐parametric Bayesian estimation of an intensity on the real line is generalized to cover spatial processes. The method is based on a model approximation where the approximating intensities have the structure of a piecewise constant function. Random step functions on the plane are generated using Voronoi tessellations of random point patterns. Smoothing between nearby intensity values is applied by means of a Markov random field prior in the spirit of Bayesian image analysis. The performance of the method is illustrated in examples with both real and simulated data.