Mixture Modeling for Marked Poisson Processes
Mixture Modeling for Marked Poisson Processes
复制标题
标记泊松过程的混合建模
DOI：
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复制
发表时间：
2010
期刊：
影响因子：
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通讯作者：
A. Kottas
中科院分区：
文献类型：
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作者：
Matt Taddy;A. Kottas
我们提出了一个通用的建模框架，在时间或空间上观察到的标记泊松过程。建模方法利用了非齐次泊松过程强度与密度函数的连接。这个密度的非参数Dirichlet过程的混合物，结合非参数或半参数模型的标记分布，产生灵活的先验模型标记泊松过程。特别是，我们专注于完全非参数模型配方，建立标记密度和强度函数从联合非参数混合物，并提供这些技术的直接应用指南。这样的模型的一个关键特征是，它们可以产生灵活的推理多元标记的条件分布，而不需要一个复杂的依赖方案的规格。我们解决有关的狄利克雷过程的混合内核的选择问题，并制定事先规范和后验模拟充分推断标记泊松过程的泛函的方法。此外，我们讨论了一种模型检查的方法，可以用来评估和比较不同的模型规格下提出的框架的拟合优度。模拟和真实的数据集的方法说明。
We propose a general modeling framework for marked Poisson processes observed over time or space. The modeling approach exploits the connection of the nonhomogeneous Poisson process intensity with a density function. Nonparametric Dirichlet process mixtures for this density, combined with nonparametric or semiparametric modeling for the mark distribution, yield flexible prior models for the marked Poisson process. In particular, we focus on fully nonparametric model formulations that build the mark density and intensity function from a joint nonparametric mixture, and provide guidelines for straightforward application of these techniques. A key feature of such models is that they can yield flexible inference about the conditional distribution for multivariate marks without requiring specification of a complicated dependence scheme. We address issues relating to choice of the Dirichlet process mixture kernels, and develop methods for prior specification and posterior simulation for full inference about functionals of the marked Poisson process. Moreover, we discuss a method for model checking that can be used to assess and compare goodness of fit of different model specifications under the proposed framework. The methodology is illustrated with simulated and real data sets.
影响因子：
4.4
作者：
Ji C;Merl D;Kepler TB;West M
通讯作者：
West M