BAYESIAN INFERENCE TO PREDICT NEXT LOWER RECORDS: AN APPLICATION IN EPIDEMIOLOGICAL MODELING
Pak A. Makhdoom I. Mahmoudi M. Mosavi A.
2025World Scientific
Fractals
2025
Various fluctuations were observed in the reported deaths and confirmed cases of COVID-19. It is, however, essential to predict lower record rates of morbidity and mortality based on the available data. In this study, we use a Bayesian setting to obtain the point and interval predictions of the future lower records of confirmed cases and deaths. To do this, we adopt a Markov Chain Monte Carlo (MCMC) algorithm for computing Bayes predictors and explain all the tricks required to implement them in detail. We also provide real examples of applications of the proposed algorithm to predict future lower records. The results demonstrate the proximity of the predicted values to the true lower records of datasets.
Artificial Intelligence , Bayesian , Data Science , Markov Chain Monte Carlo , Mathematical Modeling , Soft Computing
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Department of Computer Sciences, Shahrekord University, Shahrekord, Iran
Department of Statistics, Payame Noor University (PNU), Tehran, Iran
Department of Statistics, Faculty of Science, Fasa University, Fasa, Iran
Obuda University, Budapest, Hungary
University of Public Service, Budapest, Hungary
Abylkas Saginov Karaganda Technical University, Karaganda, Kazakhstan
Department of Computer Sciences
Department of Statistics
Department of Statistics
Obuda University
University of Public Service
Abylkas Saginov Karaganda Technical University
10 лет помогаем публиковать статьи Международный издатель
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