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Researches on the Method of Bayesian Models Selections and Averages

机译:贝叶斯模型选择方法和平均值的研究

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For improving the analyses results of Bayesian model selections and averages, by which the uncertainties and risks of analyses results of natural disasters can be reduced or removed, two important problems about Bayes Factors computation, i.e. determination of parameters' prior distribution and numerical integration of models, are mainly discussed and resolved firstly, then a new method of Bayes Factors computation has been proposed, finally the accuracy and effectiveness of this new method have been confirmed and compared with BIC by Monte-Carlo tests. Results show that the new method is more effective and reliable than BIC, since it can overcome the influence of many unfavorable factors by analyzing and describing the uncertainties of model's parameters.
机译:用于改进贝叶斯模型选择和平均值的分析,通过这种情况,可以减少或消除分析的分析结果的不确定性和风险,这两个重要问题是关于贝叶斯因子计算的两个重要问题,即确定参数的现有分布和模型的数值集成,主要讨论和首先解决,然后提出了一种新的贝叶斯因子计算方法,最后已经确认了这种新方法的准确性和有效性并通过Monte-Carlo试验进行了比较。结果表明,新方法比BIC更有效可靠,因为它可以通过分析和描述模型参数的不确定性来克服许多不利因素的影响。

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