论文标题
BINSPP：贝叶斯推断的R套件用于Neyman-Scott Point过程，具有复杂的不均匀性结构
binspp: An R Package for Bayesian Inference for Neyman-Scott Point Processes with Complex Inhomogeneity Structure
论文作者
论文摘要
Neyman-Scott Point过程是一个广泛使用的点过程模型，易于解释且易于扩展，以包括各种类型的不均匀性。然后，对这种复杂模型的推断是复杂且快速的方法，例如最小对比度方法或复合可能性方法无法提供准确的估计或完全失败。因此，我们引入了贝叶斯MCMC方法，以推断Neymann-Scott点过程模型在以下任何或所有模型组件中具有不均匀性：群集中心的过程，群集中的平均点数，集群的传播。我们还将Neyman-Scott点过程扩展到过度分散或分散的簇大小的情况，并为其推断提供贝叶斯MCMC算法。 R软件包BINSPP在易于处理的实现中提供了这些估计方法，并提供详细的图形输出，包括所有模型参数和进一步诊断图的跟踪图。所有不均匀性均由空间协变量建模，并提供了相应回归参数的贝叶斯推断。
The Neyman-Scott point process is a widely used point process model which is easily interpretable and easily extendable to include various types of inhomogeneity. The inference for such complex models is then complicated and fast methods, such as minimum contrast method or composite likelihood approach do not provide accurate estimates or fail completely. Therefore, we introduce Bayesian MCMC approach for the inference of Neymann-Scott point process models with inhomogeneity in any or all of the following model components: process of cluster centers, mean number of points in a cluster, spread of the clusters. We also extend the Neyman-Scott point process to the case of overdispersed or underdispersed cluster sizes and provide a Bayesian MCMC algorithm for its inference. The R package binspp provides these estimation methods in an easy to handle implementation, with detailed graphical output including traceplots for all model parameters and further diagnostic plots. All inhomogeneities are modelled by spatial covariates and the Bayesian inference for the corresponding regression parameters is provided.
