论文标题
分层log log高斯cox过程，用于不均匀森林的再生
Hierarchical log Gaussian Cox process for regeneration in uneven-aged forests
论文作者
论文摘要
我们为点模式提出了一个分层log高斯COX过程（LGCP），其中一组X会影响另一组点y，但反之亦然。我们使用该模型来研究大树对幼苗位置的影响。在模型中，X中的每个点都有参数影响内核或信号，它们共同形成了影响场。在参数上有条件地，影响场充当模型强度的空间协变量，强度本身是参数的非线性函数。观察窗口外的点可能会影响窗口内部的影响场。我们提出了一个边缘校正，以说明此丢失的数据。使用马尔可夫链蒙特卡洛（MCMC）在贝叶斯框架中估算模型的参数，其中拉普拉斯近似用于LGCP模型的高斯田地。所提出的模型用于分析大树对芬兰不均匀森林林分再生的成功的影响。
We propose a hierarchical log Gaussian Cox process (LGCP) for point patterns, where a set of points x affects another set of points y but not vice versa. We use the model to investigate the effect of large trees to the locations of seedlings. In the model, every point in x has a parametric influence kernel or signal, which together form an influence field. Conditionally on the parameters, the influence field acts as a spatial covariate in the intensity of the model, and the intensity itself is a non-linear function of the parameters. Points outside the observation window may affect the influence field inside the window. We propose an edge correction to account for this missing data. The parameters of the model are estimated in a Bayesian framework using Markov chain Monte Carlo (MCMC) where a Laplace approximation is used for the Gaussian field of the LGCP model. The proposed model is used to analyze the effect of large trees on the success of regeneration in uneven-aged forest stands in Finland.
