Parameter Identification in a Probabilistic Setting
Parameter Identification in a Probabilistic Setting
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DOI：
10.1016/j.engstruct.2012.12.029
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发表时间：
2012-01
期刊：
影响因子：
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通讯作者：
B. Rosic;A. Kucerová;J. Sýkora;O. Pajonk;A. Litvinenko;H. Matthies
中科院分区：
文献类型：
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作者：
B. Rosic;A. Kucerová;J. Sýkora;O. Pajonk;A. Litvinenko;H. Matthies
待识别的参数被描述为随机变量，随机性反映了真实值的不确定性，允许通过贝叶斯定理纳入新的信息。这种描述有两个组成部分，可测函数或随机变量，以及概率测度。一组方法更新度量，另一组方法更改函数。我们将两者与随机问题的谱表示方法连接起来，并引入一个计算过程，无需任何采样，其工作完全确定性，并且快速可靠。我们展示的一些例子具有高度非线性和非光滑的行为，并使用非高斯测量。
The parameters to be identified are described as random variables, the randomness reflecting the uncertainty about the true values, allowing the incorporation of new information through Bayes’s theorem. Such a description has two constituents, the measurable function or random variable, and the probability measure. One group of methods updates the measure, the other group changes the function. We connect both with methods of spectral representation of stochastic problems, and introduce a computational procedure without any sampling which works completely deterministically, and is fast and reliable. Some examples we show have highly nonlinear and non-smooth behaviour and use non-Gaussian measures.