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Article overview
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Exact Bayesian inference in spatio-temporal Cox processes driven by multivariate Gaussian processes | | Flávio B. Gonçalves
; Dani Gamerman
; | | Date: |
24 Apr 2015 | | Abstract: | In this paper we present a novel inference methodology to perform Bayesian
inference for spatio-temporal Cox processes where the intensity function
depends on a multivariate Gaussian process. Dynamic Gaussian processes are
introduced to allow for evolution of the intensity function over discrete time.
The novelty of the method lies on the fact that no discretisation error is
involved despite the non-tractability of the likelihood function and infinite
dimensionality of the problem. The method is based on a Markov chain Monte
Carlo algorithm that samples from the joint posterior distribution of the
parameters and latent variables of the model. A particular choice of the
dominating measure to obtain the likelihood function is shown to be crucial to
devise a valid MCMC. The models are defined in a general and flexible way but
they are amenable to direct sampling from the relevant distributions, due to
careful characterisation of its components. The models also allow for the
inclusion of regression covariates and/or temporal components to explain the
variability of the intensity function. These components may be subject to
relevant interaction with space and/or time. Simulated examples illustrate the
methodology, followed by concluding remarks. | | Source: | arXiv, 1504.6638 | | Services: | Forum | Review | PDF | Favorites | |
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