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

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

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Publication Date: 2021-12-27

DOI: 10.52589/AJMSS-C4L7KHUC

Volume/Issue: Volume 4, Issue 3 (2021)

Page No: 186 - 198

Journal: African Journal of Mathematics and Statistics Studies

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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 DataMCMCGibbs Sampling Algorithm with Metropolis-Hasting StepsAir Pollution.

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All articles are published open access under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence, which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, including for commercial purposes, provided appropriate credit is given to the original author(s) and source.

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Copyrights

Authors retain copyright in their articles and have unrestricted rights to reuse, republish, archive, share, and distribute their work after publication.
All articles are published open access under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence, which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, including for commercial purposes, provided appropriate credit is given to the original author(s) and source.