Abstract
Interval-probability (IP) is a substantial generalization of classical probability. It allows to adequately model different aspects of uncertainty without loosing the neat connection to the methodology of classical statistics. Therefore it provides a well-founded basis for data-based reasoning in the presence of uncertain knowledge. — The paper supports that claim by outlining the generalization of Neyman-Pearson-tests to IP. After introducing some basics of the theory of IP according to Weichselberger (1995, 1998) the fundamental concepts for tests are extended to IP; then the Huber-Strassen-theory is briefly reviewed in this context and related theorems for general IP are given. Finally further results are sketched.
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© 1999 Springer-Verlag Berlin · Heidelberg
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Augustin, T. (1999). On Data-Based Checking of Hypotheses in the Presence of Uncertain Knowledge. In: Gaul, W., Locarek-Junge, H. (eds) Classification in the Information Age. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60187-3_11
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DOI: https://doi.org/10.1007/978-3-642-60187-3_11
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