201. Bayesian Derivative Order Estimation for a Fractional Logistic Model
- Author
-
Alberto Fleitas-Imbert, Martin P. Arciga-Alejandre, Jorge Sanchez-Ortiz, and Francisco J. Ariza-Hernandez
- Subjects
Mean squared error ,General Mathematics ,lcsh:Mathematics ,Bayesian probability ,grünwald-lenikov method ,Markov chain Monte Carlo ,010103 numerical & computational mathematics ,Derivative ,Inverse problem ,lcsh:QA1-939 ,01 natural sciences ,Synthetic data ,Fractional calculus ,010101 applied mathematics ,symbols.namesake ,growth model ,Computer Science (miscellaneous) ,symbols ,Applied mathematics ,0101 mathematics ,bayesian analysis ,Likelihood function ,Engineering (miscellaneous) ,Mathematics - Abstract
In this paper, we consider the inverse problem of derivative order estimation in a fractional logistic model. In order to solve the direct problem, we use the Grü, nwald-Letnikov fractional derivative, then the inverse problem is tackled within a Bayesian perspective. To construct the likelihood function, we propose an explicit numerical scheme based on the truncated series of the derivative definition. By MCMC samples of the marginal posterior distributions, we estimate the order of the derivative and the growth rate parameter in the dynamic model, as well as the noise in the observations. To evaluate the methodology, a simulation was performed using synthetic data, where the bias and mean square error are calculated, the results give evidence of the effectiveness for the method and the suitable performance of the proposed model. Moreover, an example with real data is presented as evidence of the relevance of using a fractional model.
- Published
- 2020