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In statistics, the Q-function is the tail distribution function of the standard normal distribution. [ 1 ] [ 2 ] In other words, Q ( x ) {\displaystyle Q(x)} is the probability that a normal (Gaussian) random variable will obtain a value larger than x {\displaystyle x} standard deviations.
Using the fact that (,) =, the generalized Marcum Q-function can alternatively be defined as a finite integral as (,) = (+) ().However, it is preferable to have an integral representation of the Marcum Q-function such that (i) the limits of the integral are independent of the arguments of the function, (ii) and that the limits are finite, (iii) and that the integrand is a Gaussian function ...
In mathematics and statistics, Q-distribution or q-distribution may refer to: Q-function, the tail distribution function of the standard normal distribution; The studentized range distribution, which is the distribution followed by the q-statistic (lowercase Q; the Q-statistic with uppercase Q, from the Box-Pierce test or Ljung-Box test, follows the chi-squared distribution)
Q-learning can identify an optimal action-selection policy for any given finite Markov decision process, given infinite exploration time and a partly random policy. [2] "Q" refers to the function that the algorithm computes: the expected reward—that is, the quality—of an action taken in a given state. [3]
Q.E.D.-- Qallalin tiles-- Qashani-- QCMA-- QED manifesto-- QED project-- QIP (complexity)-- QMA-- QR algorithm-- QR decomposition-- QST (genetics)-- Quad-edge ...
Q-analogs are a certain type of generalization of many common functions; they can be interpreted as quantizations. There is a list of q -analogs further categorized by the area. The main article for this category is Q-analogs .
In mathematical physics and probability and statistics, the Gaussian q-distribution is a family of probability distributions that includes, as limiting cases, the uniform distribution and the normal (Gaussian) distribution.
The use of the log likelihood can be generalized to that of the α-log likelihood ratio. Then, the α-log likelihood ratio of the observed data can be exactly expressed as equality by using the Q-function of the α-log likelihood ratio and the α-divergence. Obtaining this Q-function is a generalized E step. Its maximization is a generalized M ...