Abstract
This chapter is dedicated to scope of the application of Importance Sampling Techniques to the design phase of Neyman-Pearson Neural Detectors. This phase usually requires the application of Monte- Carlo trials in order to estimate some performance parameters. The classical Monte-Carlo method is suitable to estimate high event probabilities but not suitable to estimate very low event probabilities (say, 10-4 or less). For estimations of very low false-alarm probabilities (or error probabilities), a modified Monte-Carlo technique, so-called Importance Sampling (IS) technique, is then considered.
This research has been supported by the National Spanish Research Institution “Comisión Interministerial de Ciencia y Tecnología-CICYT” as part of the project TIC2002-03519
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Andina, D., Torres-Alegre, S., Vega-Corona, A., Álvarez-Vellisco, A. (2003). Advances in Neyman-Pearson Neural Detectors Design. In: Mira, J., Álvarez, J.R. (eds) Artificial Neural Nets Problem Solving Methods. IWANN 2003. Lecture Notes in Computer Science, vol 2687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44869-1_32
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DOI: https://doi.org/10.1007/3-540-44869-1_32
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