Advances in Neyman-Pearson Neural Detectors Design

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 MonteCarlo 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.