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Let denote -pixels instances of some random variables and.
In engineering practice, the probability distributions of some random variables are often unknown and the only available information about these may be their statistical moments.
To determine each one of the objective functions of the model, it is required to determine the CDF of the sum of some random variables.
Thus, we have the following picture: The model (A,Δ) can always be obtained from a classical model simply by omitting some random variables, and identifying outcomes that can no longer be distinguished by those that remain.
The risk of failure of a system can be expressed by the probability R = Pr X2 < X1), where X1 and X2 are some random variables representing the stress and the strength experienced by the system.
Second, in contrast to previous literature, we also consider a case where CPs that describe binary erasure channels (BECs) are realizations of some random variables, and only their probability distributions, and not their exact values, are known to the transmitter and the receiver.
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In the present paper, we continue to study the PAC-Bayesian inequalities for some random variable sequence.
As pointed out above, such a case may be encountered if only the magnitude of some random variable is recorded, without taking into consideration its sign (that is, its direction).
Theorem A Let { X n k, 1 ≤ k ≤ n, n ≥ 1 } be an array of rowwise independent random variables with E X n k = 0. Suppose that { X n k, 1 ≤ k ≤ n, n ≥ 1 } are uniformly bounded by some random variable X.
In order to infer a possible positive response to perturbation, we observe some random variable of this system, i.e. an observable that we will name O.
One such approach, referred to as "bag of edges", is to treat each edge in the brain graph as a sample from some random variable.
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