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- W2799580173 abstract "Bayesian state estimators, unlike maximum likelihood estimators, generate a state estimate and a probability density function (PDF) representing the predicted uncertainty of the estimate. While it is relatively straightforward to determine the accuracy of the state estimate, verifying the accuracy of the predicted uncertainty is more difficult, especially when the uncertainty is time-varying. In this work, we review two prior techniques that verify the predicted uncertainty of an estimator. We show that each technique verifies the accuracy of the estimator’s uncertainty by checking if normalized state estimates follow a chi-squared distribution. If these normalized samples do not follow the correct chi-squared distribution, one can conclude that the predicted uncertainty is unreliable. In this work, we propose to use goodness-of-fit tests to determine if normalized state estimates do not follow the correct distribution. Our results demonstrate that one of the prior techniques achieves superior performance if the true uncertainty is Gaussian. When the true uncertainty is non-Gaussian, however, our proposed goodness-of-fit method demonstrates higher discriminative power." @default.
- W2799580173 created "2018-05-17" @default.
- W2799580173 creator A5003563590 @default.
- W2799580173 creator A5084117282 @default.
- W2799580173 date "2018-04-27" @default.
- W2799580173 modified "2023-09-24" @default.
- W2799580173 title "Verifying the predicted uncertainty of Bayesian estimators" @default.
- W2799580173 doi "https://doi.org/10.1117/12.2304954" @default.
- W2799580173 hasPublicationYear "2018" @default.
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