论文标题
近似贝叶斯的贝叶斯推断，以表现出规律性和随机聚集的空间点过程模型
Approximate Bayesian inference for a spatial point process model exhibiting regularity and random aggregation
论文作者
论文摘要
在本文中，我们提出了一个具有聚合和排斥的双随机空间点过程模型。该模型结合了Strauss流程背后的想法和原木高斯Cox流程。该模型的可能性在封闭形式中无法表达，但很容易模拟模型下的实现。因此，我们解释了如何使用近似贝叶斯计算（ABC）对此模型进行统计推断。我们建议一种基于后验预测和全局信封的模型验证方法。我们使用模拟点模式和真实数据示例说明了ABC过程和模型验证方法。
In this paper, we propose a doubly stochastic spatial point process model with both aggregation and repulsion. This model combines the ideas behind Strauss processes and log Gaussian Cox processes. The likelihood for this model is not expressible in closed form but it is easy to simulate realisations under the model. We therefore explain how to use approximate Bayesian computation (ABC) to carry out statistical inference for this model. We suggest a method for model validation based on posterior predictions and global envelopes. We illustrate the ABC procedure and model validation approach using both simulated point patterns and a real data example.
