Robust Bayesian Methods

Daniel Thorburn
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Daniel Thorburn: University of Stockholm, Department of Statistics

A chapter in Probability and Bayesian Statistics, 1987, pp 463-470 from Springer

Abstract: Abstract 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.

Keywords: Posterior Distribution; Covariance Function; Gaussian Process; True Distribution; Choose Model (search for similar items in EconPapers)
Date: 1987
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DOI: 10.1007/978-1-4613-1885-9_47

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