A Spatial Nonhomogeneous Poisson Process Model Using Bayesian Approach on a Space-Time Geostatistical Data

Anggun Yuliarum Qur'ani; Subanar

doi:10.52589/AJMSS-C4L7KHUC

A Spatial Nonhomogeneous Poisson Process Model Using Bayesian Approach on a Space-Time Geostatistical Data | AB Journals

A Spatial Nonhomogeneous Poisson Process Model Using Bayesian Approach on a Space-Time Geostatistical Data

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African Journal of Mathematics and Statistics Studies

Vol. 4Issue 32021pp. 186–198Published 27 December 2021

DOI 10.52589/AJMSS-C4L7KHUC

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Abstract:

In this research, we propose the nonhomogeneous Poisson process on geostatistical data by adding a time component to be applied in the study case of air pollution in the Special Region of Yogyakarta. We use the Bayesian approach to inference the model using the MCMC method. And to generate samples of the posterior distribution, we wield the Metropolis-Hastings algorithm, and we obtained it has good convergence for this case. And to show the goodness of fit of this model, we had the value of DIC.

Keywords:Spatial Nonhomogeneous Poisson Process (SNHPP)Space-time Geostatistical Data MCMC Gibbs Sampling Algorithm with Metropolis-Hasting Steps Air Pollution.

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