Guided Bayesian optimal experimental design
Guided Bayesian optimal experimental design
复制标题
贝叶斯引导优化实验设计
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
H. Djikpesse
中科院分区：
文献类型：
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作者：
M. Khodja;M. Prange;H. Djikpesse
贝叶斯方法描述设计的实验或调查，将最佳地补充所有以前可用的信息。这种方法使用强先验信息来线性化问题，并指导设计最大限度地减少预测的不确定性，在未来的实验解释。先验信息可能与模型参数或观测噪声相关。在没有先验信息的情况下，这种方法简化为D-最优性准则的快速递归实现。合成地球物理层析成像的例子来说明这种方法的好处。设计的模型表示的依赖关系也进行了讨论。
A Bayesian methodology is described for designing experiments or surveys that will optimally complement all previously available information. This methodology uses strong prior information to linearize the problem, and to guide the design toward maximally reducing forecast uncertainties in the interpretation of the future experiment. The prior information could possibly be correlated among model parameters or the observation noise. With no prior information this approach reduces to the fast recursive implementation of the D-optimality criterion. Synthetic geophysical tomography examples are used to illustrate the benefits of this approach. The dependence of the design on the model representation is also discussed.