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Detection theory or signal detection theory is a means to measure the ability to differentiate between information-bearing patterns (called stimulus in living organisms, signal in machines) and random patterns that distract from the information (called noise, consisting of background stimuli and random activity of the detection machine and of the nervous system of the operator).
Natural scene statistics are the basis for calculating ideal performance in natural and pseudo-natural tasks. This calculation tends to incorporate elements of signal detection theory , information theory , or estimation theory .
His research interests lie in the areas of stochastic analysis, statistical signal processing and information theory, and their applications in a number of fields including wireless networks, social networks, and smart grid. This research work has attracted over 10,000 citations. [10] He has published a book on Signal Detection and Estimation. [11]
From 1965 to 1967, Helstrom served as Associate Editor for Detection Theory on the editorial board of the IEEE Transactions on Information Theory; 1967 to 1971 he served as Editor-in-Chief of the same journal. He was a member of Phi Beta Kappa.
The sensitivity index or discriminability index or detectability index is a dimensionless statistic used in signal detection theory. A higher index indicates that the signal can be more readily detected.
In medicine and statistics, sensitivity and specificity mathematically describe the accuracy of a test that reports the presence or absence of a medical condition. If individuals who have the condition are considered "positive" and those who do not are considered "negative", then sensitivity is a measure of how well a test can identify true ...
A minimum detectable signal is a signal at the input of a system whose power allows it to be detected over the background electronic noise of the detector system. It can alternately be defined as a signal that produces a signal-to-noise ratio of a given value m at the output. In practice, m is usually chosen to be greater than unity.
A psychometric function is an inferential psychometric model applied in detection and discrimination tasks. It models the relationship between a given feature of a physical stimulus, e.g. velocity, duration, brightness, weight etc., and forced-choice responses of a human or animal test subject.