Outlier Tolerant Parameter Estimation

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Abstract

Real world does not only provide noisy instead of perfect data. Every experimentalist has now and then to deal with outliers. The situation is simple if isolated points stick out of the general trend by a large amount. Arguments can then usually be found why such a point should be disregarded. The situation becomes critical if the outliers are not that obvious. This is usually the case for parameter space dimensions ≥ 3. We present a Bayesian solution to the outlier problem which assumes that the uncertainties assigned to the experimental data are only estimates of the true error variances.
Original languageEnglish
Title of host publicationMaximum Entropy and Bayesian Methods Garching, Germany 1998
EditorsWolfgang von der Linden, Volker Dose, Rainer Fischer, Roland Preuss
PublisherSpringer Netherlands
Pages47-56
Number of pages10
ISBN (Print)978-94-010-5982-4 978-94-011-4710-1
Publication statusPublished - 1999

Publication series

NameFundamental Theories of Physics
PublisherSpringer Netherlands

Keywords

  • Artificial Intelligence (incl. Robotics), Coding and Information Theory, Discrete Mathematics in Computer Science, duff data, Outlier, parameter estimation, Probability Theory and Stochastic Processes, Statistics, general

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  • Cite this

    Dose, V., & Linden, W. V. D. (1999). Outlier Tolerant Parameter Estimation. In W. V. D. Linden, V. Dose, R. Fischer, & R. Preuss (Eds.), Maximum Entropy and Bayesian Methods Garching, Germany 1998 (pp. 47-56). (Fundamental Theories of Physics). Springer Netherlands.