Statistical Regularization of Inverse Problems
Statistical Regularization of Inverse Problems
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DOI：
10.1137/s0036144500358232
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发表时间：
2001-02
期刊：
影响因子：
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通讯作者：
L. Tenorio
中科院分区：
文献类型：
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作者：
L. Tenorio
在实验科学中，我们经常需要解决逆问题。也就是说，我们希望从间接的噪声观测中获得关于物理系统内部结构的信息。通常问题不在于是否存在解决方案;相反，有太多的解决方案使数据适合所选的公差水平。我们的目标是使用先验信息来确定一个物理上有意义的解决方案。在这里，我们提出了一些出现的基本问题。我们描述了可用于找到反演估计的方法以及评估其性能的方法。
In experimental sciences we often need to solve inverse problems. That is, we want to obtain information about the internal structure of a physical system from indirect noisy observations. Often the problem is not whether a solution exists; on the contrary, there are too many solutions that fit the data to a chosen tolerance level. The goal is to use prior information to determine a physically meaningful solution. Here, we present some of the basic questions that arise. We describe methods that can be used to find inversion estimates as well as ways to assess their performance.