Bayesian inference for dynamical systems.
| Autor: | Roda WC; Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2G1, Canada. |
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| Jazyk: | angličtina |
| Zdroj: | Infectious Disease Modelling [Infect Dis Model] 2020 Jan 10; Vol. 5, pp. 221-232. Date of Electronic Publication: 2020 Jan 10 (Print Publication: 2020). |
| DOI: | 10.1016/j.idm.2019.12.007 |
| Abstrakt: | Bayesian inference is a common method for conducting parameter estimation for dynamical systems. Despite the prevalent use of Bayesian inference for performing parameter estimation for dynamical systems, there is a need for a formalized and detailed methodology. This paper presents a comprehensive methodology for dynamical system parameter estimation using Bayesian inference and it covers utilizing different distributions, Markov Chain Monte Carlo (MCMC) sampling, obtaining credible intervals for parameters, and prediction intervals for solutions. A logistic growth example is given to illustrate the methodology. Competing Interests: I wish to confirm that there are no known conflicts of interest associated with these lecture notes. (© 2019 The Author.) |
| Databáze: | MEDLINE |
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