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- W4382602289 abstract "This chapter addresses techniques to isolate the set of identifiable or influential parameters in models. These techniques are often termed subset selection, active subspace, or essential subspace methods. Parameter selection is critical for the model calibration techniques detailed in Chapters 7 and 8 and to reduce the dimensionality of models for uncertainty propagation. For model calibration, unidentifiable parameters cannot be uniquely estimated by frequentist or Bayesian inference using noninformative priors. For example, we illustrated in Example 3.2 that the parameters q = [m, c, k] for the spring model (3.5) cannot be uniquely determined using displacement data, whereas the reformulated model parameters and are identifiable. For models such as those arising in systems biology or neutron transport, the number of parameters can be in the millions, so techniques to isolate influential inputs are critical to reduce the dimensionality of surrogate models used for model calibration or uncertainty propagation." @default.
- W4382602289 created "2023-06-30" @default.
- W4382602289 date "2013-01-01" @default.
- W4382602289 modified "2023-09-24" @default.
- W4382602289 title "Chapter 6: Parameter Selection Techniques" @default.
- W4382602289 doi "https://doi.org/10.1137/1.9781611973228.ch6" @default.
- W4382602289 hasPublicationYear "2013" @default.
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