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Theorem A Let { X, X n, n ≥ 1 } be an independent and identically distributed sequence with the same distribution function F ( x ), and let { w n, n ≥ 1 } be a sequence of positive numbers.
Let be a Bernoulli distributed sequence defined by (2.4).
The matrix X(k) is assumed an independent and identically distributed sequence matrix [2, 27].
Then, a Bernoulli distributed sequence is introduced to describe the packet dropout phenomenon of the wireless communication.
The basic approach is first illustrated for the case of an independent identically distributed sequence of samples from a univariate mixture of M classes (symbols).
We assume an independent and identically distributed sequence for q(n) with autocorrelation matrix ( mathbf{Q}={sigma}_q^2mathbf{I} ) [27].
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The uncertainty is assumed to satisfy a dissipative inequality, and the multiple measurement and control packet dropouts are described by two independent Bernoulli distributed sequences.
As an alternative to the random methods, it has been suggested that lower error and improved convergence may be obtained by replacing the pseudo-random sequences with more uniformly distributed sequences known as quasi-random.
The result of Theorem A for independent and identically distributed sequences has been generalized to some dependent sequences, such as negatively associated sequences, negatively superadditive dependent sequences, ρ ˜ -mixing sequences, φ ˜ -mixing sequences, and so forth.
In addition, Gut and Stadtmüller (2011 [1]) and Qiu and Chen (2014 [2]) obtained, respectively, complete convergence and complete moment convergence theorems for independent identically distributed
We expected that these effects would be decreased by averaging the data across random, uniformly distributed sequences of stimuli.
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