Abstract
As Dempster-Shafer theory spreads in different applications fields involving complex systems, the need for algorithms randomly generating mass functions arises. As such random generation is often perceived as secondary, most proposed algorithms use procedures whose sample statistical properties are difficult to characterize. Thus, although they produce randomly generated mass functions, it is difficult to control the sample statistical laws. In this paper, we briefly review classical algorithms, explaining why their statistical properties are hard to characterize, and then provide simple procedures to perform efficient and controlled random generation.
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© 2012 Springer-Verlag Berlin Heidelberg
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Burger, T., Destercke, S. (2012). Random Generation of Mass Functions: A Short Howto. In: Denoeux, T., Masson, MH. (eds) Belief Functions: Theory and Applications. Advances in Intelligent and Soft Computing, vol 164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29461-7_17
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DOI: https://doi.org/10.1007/978-3-642-29461-7_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29460-0
Online ISBN: 978-3-642-29461-7
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