true. and thus are maximized by extending over the region where This is accomplished by deciding H2 in case and H1 otherwise, where L(y) is the so-defined likelihood ratio. Normal distribution models Das and Geisler extended the results of signal detection theory for normally distributed stimuli, and derived methods of computing the error rate and confusion matrix for ideal observers and non-ideal observers for detecting and categorizing univariate and multivariate normal signals from two or more categories. See also Binary classification Constant false alarm rate Decision theory Demodulation Detector (radio) Estimation theory Just-noticeable difference Likelihood-ratio test Modulation Neyman–Pearson lemma Psychometric function Receiver operating characteristic Statistical hypothesis testing Statistical signal processing Two-alternative forced choice Type I and type II errors References Coren, S., Ward, L.M., Enns, J. T. (1994) Sensation and Perception. (4th Ed.) Toronto: Harcourt Brace. Kay, SM. Fundamentals of Statistical Signal Processing: Detection Theory () McNichol, D. (1972) A Primer of Signal Detection Theory. London: George Allen & Unwin. Van Trees HL. Detection, Estimation, and Modulation Theory, Part 1 (; website) Wickens, Thomas D., (2002) Elementary Signal Detection Theory. New York: Oxford University Press. () External links A Description of Signal Detection Theory An application of SDT to safety Signal Detection Theory by Garrett Neske, The Wolfram Demonstrations Project Lecture by Steven Pinker Signal processing Telecommunication theory Psychophysics Mathematical psychology