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The Fisher information matrix summarizes the amount of information in the data relative to the quantities of interest. There are many applications of the information matrix in modeling, systems analysis, and estimation, including confidence region calculation, input design, prediction bounds, and "noninformative" priors for Bayesian analysis. This article reviews some basic principles associated with the information matrix, presents a resamplingbased method for computing the information matrixdoi:10.1198/106186005x78800 fatcat:do6tcuxjujdfxhhd73inwjx2bq