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The normal deviate mapping (or normal quantile function, or inverse normal cumulative distribution) is given by the probit function, so that the horizontal axis is x = probit(P fa) and the vertical is y = probit(P fr), where P fa and P fr are the false-accept and false-reject rates.
The term video refers to the resulting signal being appropriate for display on a cathode ray tube, or "video screen". The role of the constant false alarm rate circuitry is to determine the power threshold above which any return can be considered to probably originate from a target as opposed to one of the spurious sources.
Given that the probability distributions for both true positive and false positive are known, the ROC curve is obtained as the cumulative distribution function (CDF, area under the probability distribution from to the discrimination threshold) of the detection probability in the y-axis versus the CDF of the false positive probability on the x-axis.
The false coverage rate (FCR) is, in a sense, the FDR analog to the confidence interval. FCR indicates the average rate of false coverage, namely, not covering the true parameters, among the selected intervals. The FCR gives a simultaneous coverage at a level for all of the parameters considered in the problem.
In Boolean logic, the majority function (also called the median operator) is the Boolean function that evaluates to false when half or more arguments are false and true otherwise, i.e. the value of the function equals the value of the majority of the inputs.
The split normal distribution is most directly defined in terms of joining scaled sections of the density functions of different normal distributions and rescaling the density to integrate to one. The truncated normal distribution results from rescaling a section of a single density function.
On the INRIA set, the C-HOG and R-HOG descriptors produced a detection miss rate of roughly 0.1 at a 10 −4 false positive rate. The generalized Haar wavelets represent the next highest performing approach: they produced roughly a 0.01 miss rate at a 10 −4 false positive rate on the MIT set, and roughly a 0.3 miss rate on the INRIA set. The ...
This is more complex than maximum likelihood sequence estimation and requires a known distribution (in Bayesian terms, a prior distribution) for the underlying signal. In this case the estimate of {x(t)} is defined to be a sequence of values which maximize the functional = (),