See Significance Level ˇ2, See Kurtosis , See Unnormalized Skewness 1, See
Index ˛, see significance level deep learning, 192 ˇ2, see kurtosis Asimov dataset, 231 , see unnormalized skewness asymmetric errors, 99, 110 1, see skewness combination of, 123 2, see excess asymptotic formulae for test statistics, ", see efficiency 231 . see Gaussian average value or signal average value strength continuous case, 27 , see correlation coefficient discrete case, 12 , see standard deviation or Gaussian standard in Bayesian inference, 69 deviation , see lifetime ˚, see Gaussian cumulative distribution back propagation, neural network, 191 2 background distribution, 32 dependence of Feldman–Cousins upper method, 114 limits, 218, 220 binned case, 119 determination from control regions, in multiple dimensions, 132 129 random variable, 32, 114, 120 fluctuation for significance level, 205 Baker–Cousins, 120 in convolution and unfolding, 160 Neyman’s, 119 modeling in extended likelihood, 107 Pearson’s, 119 modeling with Argus function, 43 , see sample space rejection in hypothesis test, 176 3 evidence, 207 treatment in iterative unfolding, 171 5 observation, 207 uncertainty in significance evaluation, 209 uncertainty in test statistic, 227, 236 Baker–Cousins 2, 120 activation function, 191 Bayes factor, 73 adaptive boosting, 198 Bayes’ theorem, 59 AI, artificial intelligence, 195 learning process, 67 alternative hypothesis, 175 Bayesian Anderson–Darling test, 184 inference, 68 Argus function, 43 probability, 59, 64 artificial intelligence, 195 visual derivation, 60 artificial neural network, 181, 190 unfolding, 166 © Springer International
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