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Model Selection and Parameter Estimation for Fractional SIR Model Based on the Combination of Reinforcement Learning
Peiqi Chen1, Wei Gu1
1School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, Hubei, P.R. China.
Abstract:
A novel algorithm is proposed to increase the effectiveness of model selection and parameter estimation for the fractional susceptible-infected-recovered model. It combines reinforcement learning (RL) and approximate Bayesian computation sequential Monte Carlo (ABC-SMC) instead of ABC to improve the process of model selection and parameter estimation, where RL is used for model selection and ABC-SMC is exploited for parameter estimation. Numerical simulations illustrate that the combined algorithm (RL-ABC-SMC) significantly outperforms the ABC-SMC algorithm in terms of model selection. Finally, we consider the application of the proposed methodology.
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