Bayesian Noise Modelling for State Estimation of the Spread of COVID-19 in Saudi Arabia with Extended Kalman Filters

Bayesian Noise Modelling for State Estimation of the Spread of COVID-19 in Saudi Arabia with Extended Kalman Filters

The epistemic uncertainty in coronavirus disease (COVID-19) model-based predictions using complex noisy data greatly affects the accuracy of pandemic trend and state estimations. Quantifying the uncertainty of COVID-19 trends caused by different unobserved hidden variables is needed to evaluate the...

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Bibliographic Details
Main Authors: Lamia Alyami, Deepak Kumar Panda, Saptarshi Das
Format: Article
Language:English
Published: MDPI AG 2023-05-01
Series:Sensors
Subjects:
Bayesian model selection
nested sampling
skew-normal distributions
Bayesian evidence
extended Kalman filter (EKF)
Online Access:https://www.mdpi.com/1424-8220/23/10/4734

Internet

https://www.mdpi.com/1424-8220/23/10/4734

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