Variational estimators for the parameters of Gibbs point process models
Variational estimators for the parameters of Gibbs point process models
复制标题
吉布斯点过程模型参数的变分估计
DOI：
--
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
D. Dereudre
中科院分区：
文献类型：
--
作者：
A. Baddeley;D. Dereudre
本文提出了一种新的参数Gibbs点过程模型与空间点模式数据集拟合的估计方法。对于空间点过程，该技术是由Almeida和Gidas开发的马尔可夫随机场的变分估计的对应物。该估计量不要求点过程密度是遗传的，因此它适用于没有条件强度的模型，包括表现出几何正则性或刚性的模型。缺点是强度参数不能估计：推断实际上是以观察点的数量为条件的。新方法比现有技术更快、更稳定，因为它不需要关于参数的模拟、数值积分或优化。
This paper proposes a new estimation technique for fitting parametric Gibbs point process models to a spatial point pattern dataset. The technique is a counterpart, for spatial point processes, of the variational estimators for Markov random fields developed by Almeida and Gidas. The estimator does not require the point process density to be hereditary, so it is applicable to models which do not have a conditional intensity, including models which exhibit geometric regularity or rigidity. The disadvantage is that the intensity parameter cannot be estimated: inference is effectively conditional on the observed number of points. The new procedure is faster and more stable than existing techniques, since it does not require simulation, numerical integration or optimization with respect to the parameters.