### 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 language | English |
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Title of host publication | Maximum Entropy and Bayesian Methods Garching, Germany 1998 |

Editors | Wolfgang von der Linden, Volker Dose, Rainer Fischer, Roland Preuss |

Publisher | Springer Netherlands |

Pages | 47-56 |

Number of pages | 10 |

ISBN (Print) | 978-94-010-5982-4 978-94-011-4710-1 |

Publication status | Published - 1999 |

### Publication series

Name | Fundamental Theories of Physics |
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Publisher | Springer 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.