Exact Bayesian Inference for Diffusion-Driven Cox Processes
成果类型：
Article
署名作者：
Goncalves, Flavio B.; Latuszynski, Krzysztof G.; Roberts, Gareth O. O.
署名单位：
Universidade Federal de Minas Gerais; University of Warwick
刊物名称：
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN/ISSBN：
0162-1459
DOI：
10.1080/01621459.2023.2223791
发表日期：
2024
页码：
1882-1894
关键词：
摘要：
In this article, 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. for this article are available online.