Abstract
Coolen [3] introduced lower and upper probabilities for m future Bernoulli random quantities, based on the number of successes in n trials and adding few structural assumptions. These results form part of the statistical approach called ‘Nonparametric Predictive Inference’. In this paper, we explore the generalization of these results for the case with data only available in the form of a set of values for the number of successes in the first n trials. A special case of such inferences occurs in applications to basic acceptance sampling problems in quality control.
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Coolen, F.P.A. (2008). On Nonparametric Predictive Inference for Bernoulli Quantities with Set-Valued Data. In: Dubois, D., Lubiano, M.A., Prade, H., Gil, M.Á., Grzegorzewski, P., Hryniewicz, O. (eds) Soft Methods for Handling Variability and Imprecision. Advances in Soft Computing, vol 48. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85027-4_11
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DOI: https://doi.org/10.1007/978-3-540-85027-4_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85026-7
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