@inbook{7c74b6712f1243758dde82bb6d0cb9ef,

title = "Outlier Tolerant Parameter Estimation",

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.",

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",

author = "V. Dose and Linden, {W. Von Der}",

note = "DOI: 10.1007/978-94-011-4710-14",

year = "1999",

language = "English",

isbn = "978-94-010-5982-4 978-94-011-4710-1",

series = "Fundamental Theories of Physics",

publisher = "Springer Netherlands",

pages = "47--56",

editor = "Linden, {Wolfgang von der} and Volker Dose and Rainer Fischer and Roland Preuss",

booktitle = "Maximum Entropy and Bayesian Methods Garching, Germany 1998",

address = "Netherlands",

}