Exact Bayesian Inference for Diffusion-Driven Cox Processes

被引：0

作者：

Goncalves, Flavio B. ^{[1
]}

Latuszynski, Krzysztof G. ^{[2
]}

Roberts, Gareth O. O. ^{[2
]}

机构：

[1] Univ Fed Minas Gerais, Belo Horizonte, Brazil

[2] Univ Warwick, Coventry, England

来源：

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION | 2023年

基金：

英国工程与自然科学研究理事会;

关键词：

Infinite dimensionality; MCMC; Poisson process; Retrospective sampling;

D O I：

10.1080/01621459.2023.2223791

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

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.

引用

页数：13

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