Robust Bayesian Methods

  • Daniel Thorburn


With robust statistics we mean methods that work well, if a chosen model is true and that are acceptable if the model is only an approximation. But if the model is far from the true one robust methods may be very bad (Huber 1980, Hampel & al 1986). Thus robust statistics should be used whenever we know that the chosen model is only an approximation to the true model.


Posterior Distribution Covariance Function Gaussian Process True Distribution Choose Model 
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  1. Andrews, D.F., Bickel, P.J., Hampel, F.R., Huber, P.J., Rogers, W,H. and Tukey, J.W., 1972, “Robust estimates of location; survey and advances”. Princeton University Press, Princeton.MATHGoogle Scholar
  2. Hampel, F.R., Ronchetti, E.V., Rousseeuw, P.J. and Stahel, W.A., 1986, “Robust statistics - the approach based on influence functions”. Wiley, New York.MATHGoogle Scholar
  3. Huber, P.J., 1981, “Robust statistics”, Wiley, New York.MATHCrossRefGoogle Scholar
  4. Thorburn, D., 1986, A Bayesian approach to density estimation. Biometrika 73:65.MathSciNetMATHCrossRefGoogle Scholar

Copyright information

© Plenum Press, New York 1987

Authors and Affiliations

  • Daniel Thorburn
    • 1
  1. 1.Department of StatisticsUniversity of StockholmStockholmSweden

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