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Bayesian computational methods for state-space models with application to SIR model
Journal of Statistical Computation & Simulation ; 93(7):1207-1223, 2023.
Article in English | Academic Search Complete | ID: covidwho-2316078
ABSTRACT
The state-space model is a powerful statistical tool to estimate linear or non-linear discrete-time dynamic systems. This model naturally leads to the estimation problem of the time-varying parameters of the discovery-time demographic version of the susceptible-infected-recovered (SIR) model that we consider. In this paper, we consider computational methods to perform Bayesian inference on state-space models for analysing time-series data. We compare the three popular Bayesian computational methods for state-space models the adaptive Metropolis-within-Gibbs algorithm, Liu and West's algorithm and variational approximation method based on Gaussian distributions. The performances of the three methods are compared based on synthetic datasets. Furthermore, we analyse the trend of the spread of COVID-19 in South Korea to point out the limitations of existing methods and derive meaningful results. [ FROM AUTHOR] Copyright of Journal of Statistical Computation & Simulation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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Full text: Available Collection: Databases of international organizations Database: Academic Search Complete Language: English Journal: Journal of Statistical Computation & Simulation Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Academic Search Complete Language: English Journal: Journal of Statistical Computation & Simulation Year: 2023 Document Type: Article