Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/619
Title: Bayesian Forecasting for Days at Risk in Kuwait due to Concentration of Particulate Matters
Other Titles: تنبؤ البيزي للأيام الخطرة في الكويت من خلال تركز الجسيمات
Authors: Sarah Motlaq Mohammad Al-Mutairi 
Supervisor: Dr. Fahimah Al-Awadhi
Keywords: PM particulate matter homogenous non-homogenous poisson Kuwait public health weather Bayesian method theory statistics
Issue Date: 2014
Publisher:  Kuwait university - college of graduate studies
Abstract: This study focuses on applying a stochastic model incorporating non homogenous Poisson process and Bayesian method. We use Markov Chain Monte Carlo (MCMC) technique to implement the model to analyze the daily concentration of (Particulate matter (PM) pollution which consists of very small liquid and solid particles floating in the air) for year 2010 from monitoring stations in Kuwait spaced overall the population centers and their surrounding areas. Using the MCMC approach we model the risk situation caused by high level of . These particulates are of greatest concern to public health in Kuwait because these are small enough to be inhaled into the deepest parts of the lung. These particles are less than 10 microns in diameter (about the thickness of a human hair) and are known as . We aim in this study to describe the hazardous situation of for year 2010 for the available data in Kuwait and forecasting new ones, in a context of reliability checking before it occurs.
URI: http://hdl.handle.net/123456789/619
Appears in Programs:0480 Statistics & Operations Research

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