论文标题
确切的贝叶斯推断扩散驱动的COX过程
Exact Bayesian inference for diffusion driven Cox processes
论文作者
论文摘要
在本文中，我们提出了一种新的方法，以对COX过程进行贝叶斯推断，其中强度函数由扩散过程驱动。新颖性在于一个事实，即尽管可能性函数和扩散的过渡密度都不涉及，但不涉及离散误差。该方法基于MCMC算法，其精确性基于回顾性抽样技术。在一些模拟示例中研究了该方法的效率，并在一些实际数据分析中说明了其适用性。
In this paper, we present a novel methodology to perform Bayesian inference for Cox processes in which the intensity function is driven by a diffusion process. The novelty lies in the fact that no discretization error is involved, despite the non-tractability of both the likelihood function and the transition density of the diffusion. The methodology is based on an MCMC algorithm and its exactness is built on retrospective sampling techniques. The efficiency of the methodology is investigated in some simulated examples and its applicability is illustrated in some real data analyzes.
