Abstract
Different models can be used to describe real-life phenomena: deterministic, probabilistic, fuzzy, models in which we have interval-valued or fuzzy-valued probabilities, etc. Models are usually not absolutely accurate. It is therefore important to know how accurate is a given model. In other words, it is important to be able to measure a mismatch between the model and the empirical data. In this chapter, we describe an approach of measuring this mismatch which is based on the notion of utility, the central notion of utility theory.
The main results of this chapter first appeared in [206]. In one of the following application chapters (Chapter 35), we show that a similar approach can be used to measure the loss of privacy.
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© 2012 Springer-Verlag Berlin Heidelberg
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Nguyen, H.T., Kreinovich, V., Wu, B., **ang, G. (2012). How Accurate Is the Input Data?. In: Computing Statistics under Interval and Fuzzy Uncertainty. Studies in Computational Intelligence, vol 393. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24905-1_30
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DOI: https://doi.org/10.1007/978-3-642-24905-1_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24904-4
Online ISBN: 978-3-642-24905-1
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