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The problem of outliers in circular data is studied from a Bayesian point of view. Susprising observations are identified by means of a predictive measure. On the basis of Box-Tiao methodology, the mean-shift model and some aspects of the contamination of the concentration parameter for a Von Mises distribution are analyzed. Intuitive aspects of the resultant weights and their applications in some classical examples are included.
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