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扩散驱动 Cox 过程的精确贝叶斯推断AI 解读

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…

多元高斯过程驱动的时空 Cox 过程精确贝叶斯推断AI 解读

Summary We present a novel inference methodology to perform Bayesian inference for spatiotemporal Cox processes where the intensity function depends on a multivariate Gaussian process. Dynamic Gaussian processes are introduced to enable evolution of the intensity function over discrete time. The novelty of the method lies on the fact that no discretization error is involved despite the non-tractability of the likelihood function and infinite dimensionality of the problem.…