Brown-Resnick过程的采样超正规谱函数
Sampling Sup-Normalized Spectral Functions for Brown-Resnick Processes
摘要：
超归一化谱函数形成最大稳定过程和帕累托过程的构建块，因此在建模空间极值中发挥重要作用。对于最流行的例子之一，布朗-雷斯尼克过程，模拟并不简单。在本文中，我们推广了两种模拟方法，通过引入新的建议密度类马尔可夫链蒙特卡罗方法和拒绝抽样。在这两种情况下，我们提供了一个关于抽样效率的建议密度的最佳选择。在一个例子中证明的程序的性能。
英文摘要：
Sup-normalized spectral functions form building blocks of max-stable and Pareto processes and therefore play an important role in modeling spatial extremes. For one of the most popular examples, the Brown-Resnick process, simulation is not straightforward. In this paper, we generalize two approaches for simulation via Markov Chain Monte Carlo methods and rejection sampling by introducing new classes of proposal densities. In both cases, we provide an optimal choice of the proposal density with respect to sampling efficiency. The performance of the procedures is demonstrated in an example.
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