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A New Algorithm of Bayesian Model Averaging Based on SCE - UA Collection Averaging

Abstract : Bayesian Model Averaging (BMA) is a statistical method used for multi-model ensemble forecast system. Firstly, the likelihood function of BMA is improved by eliminating the explicit constraint, that the sum of weights is 1, and use SCE-UA for the minimization of its, which presents a new method for solving the Bayesian model averaging, that is the BMA-SCE-UA method. With three land surface models of soil moisture simulation test of multiple numerical model. By comparing the common Expectation Maximization (EM) method with the SCE-UA method, the results show that: SCE-UA method can improve the simulation performance of soil moisture in a large extent, and the soil moisture obtained by the BMA collection simulation and observation matches well, no matter from the amplitude variation and seasonal variability, which makes it possible that generating high accuracy data set of soil moisture with the method of BMA-SCE-UA and using multiple land surface models.
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Liu Jun-Hua, Zhang Hong-Qin, Zhang Cheng-Ming, Zhao Tianyu, Ma Jing. A New Algorithm of Bayesian Model Averaging Based on SCE - UA Collection Averaging. 8th International Conference on Computer and Computing Technologies in Agriculture (CCTA), Sep 2014, Beijing, China. pp.351-358, ⟨10.1007/978-3-319-19620-6_40⟩. ⟨hal-01420251⟩



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