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autocovariance : ウィキペディア英語版
autocovariance
In probability and statistics, given a stochastic process X=(X_t), the autocovariance is a function that gives the covariance of the process with itself at pairs of time points. With the usual notation ''E''  for the expectation operator, if the process has the mean function \mu_t = E(), then the autocovariance is given by
:C_(t,s) = cov(X_t, X_s) = E(- \mu_t)(X_s - \mu_s) ) = E(X_s ) - \mu_t \mu_s.\,
Autocovariance is related to the more commonly used autocorrelation of the process in question.
In the case of a random vector X=(X_1, X_2, ... , X_n), the ''autocovariance'' would be a square ''n'' by ''n'' matrix C_ with entries C_(j,k) = cov(X_j, X_k).\,
This is commonly known as the covariance matrix or matrix of covariances of the given random vector.
== Stationarity ==

If ''X''(''t'') is stationary process, then the following are true:
:\mu_t = \mu_s = \mu \, for all ''t'', ''s''
and
:C_(t,s) = C_(s-t) = C_(\tau)\,
where
:\tau = |s - t | \,
is the lag time, or the amount of time by which the signal has been shifted.
As a result, the autocovariance becomes
:C_(\tau) = E(- \mu)(X(t+\tau) - \mu) )\,
:::: = E(X(t+\tau) ) - \mu^2\,
:::: = \sigma^2 R_(\tau) - \mu^2,\,
where R_(\tau) is the autocorrelation of the signal with variance \sigma^2 . Some authors do not normalize the autocorrelation function.〔http://ece-research.unm.edu/bsanthan/ece541/stat.pdf〕 In those literatures, C_(\tau)= R_(\tau) - \mu^2,\,.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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