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A Bifocal Measure of Expected Ambiguity in Bayesian Nonlinear Parameter Estimation

Summary: This article presents a new approach expected uncertainty calculation in Bayesian parameter estimates. Unlike the most common uncertainty calculation techniques, this new approach fully accounts for nonlinearity. Pairs of parameter estimates are analyzed, forming a bifocal measure of ambiguity that does not require any observational data. The model is equivalent to expected posterior variance for linear models and closely related for nonlinear models.

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  • Topics: Statistics
  • Keywords: Bayesian methods, Frequency distribution, Estimation, Nonlinear models, Optimal design, Parameters, Uncertainty, Variance (statistics)
  • Author: Winterfors, Emanuel; Curtis, Andrew
  • Journal: Technometrics