The posterior density of structural parameters conditioned by the measurement is obtained by a differential evolution adaptive\nMetropolis algorithm (DREAM). The surface of the formal log-likelihood measure is studied considering the uncertainty of\nmeasurement error to illustrate the problem of equifinality. To overcome the problem of equifinality, the first two derivatives of\nthe log-likelihood measure are proposed to formulate a new informal likelihood measure for the sake of improving the accuracy of\nthe estimator.Moreover, the proposed measure also reduces the standard deviation (uncertain range) of the posterior samples.The\nbenefit of the proposed approach is demonstrated by simulations on identifying the structural parameters with limit output data\nand noise polluted measurements.
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