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In a globally identifiable Bayesian system identification problem, the uncertainty of model parameters can be quantified by their “posterior covariance matrix” calculated for a particular data set. When the data is modeled to be distributed as the likelihood function (i.e., no modeling error), a statistical law analogous to the law of large numbers results, where the posterior covariance matrix is...
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