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第491行： − − The best-known importance sampling method, the Metropolis algorithm, can be generalized, and this gives a method that allows analysis of (possibly highly nonlinear) inverse problems with complex a priori information and data with an arbitrary noise distribution.
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The best-known importance sampling method, the Metropolis algorithm, can be generalized, and this gives a method that allows analysis of (possibly highly nonlinear) inverse problems with complex ''a priori'' information and data with an arbitrary noise distribution.<ref name=":63">Mosegaard, Klaus; Tarantola, Albert (1995). "Monte Carlo sampling of solutions to inverse problems" (PDF). ''J. Geophys. Res''. '''100''' (B7): 12431–12447. Bibcode:1995JGR...10012431M. doi:10.1029/94JB03097.</ref><ref name=":64">Tarantola, Albert (2005). ''Inverse Problem Theory''. Philadelphia: Society for Industrial and Applied Mathematics. ISBN <bdi>978-0-89871-572-9</bdi>.</ref>
The best-known importance sampling method, the Metropolis algorithm, can be generalized, and this gives a method that allows analysis of (possibly highly nonlinear) inverse problems with complex ''a priori'' information and data with an arbitrary noise distribution.<ref name=":63">Mosegaard, Klaus; Tarantola, Albert (1995). "Monte Carlo sampling of solutions to inverse problems" (PDF). ''J. Geophys. Res''. '''100''' (B7): 12431–12447. Bibcode:1995JGR...10012431M. doi:10.1029/94JB03097.</ref><ref name=":64">Tarantola, Albert (2005). ''Inverse Problem Theory''. Philadelphia: Society for Industrial and Applied Mathematics. ISBN <bdi>978-0-89871-572-9</bdi>.</ref>
最著名的重要性抽样方法，Metropolis–Hastings 演算法，可以推广，这提供了一种方法，允许分析(可能是高度非线性)与复杂的先验信息和数据与任意噪声分布的反问题。<ref name=":63" /><ref name=":64" />
最著名的重要性抽样方法，Metropolis–Hastings 演算法，可以推广，这提供了一种方法，允许分析(可能是高度非线性)与复杂的先验信息和数据与任意噪声分布的反问题。<ref name=":63" /><ref name=":64" />