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It remains to specify R. For a weak-sense stationary process, the covariance between random variables depends only upon their separation in time-index.
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Note that is a random variables depending on the channel realization.
The characteristics of the forwarding and waiting random variables depend on both node mobility and the routing protocol.
The surprisal of the outcome of a random variable depends only on the probability of the corresponding outcome Pr(x).
α can be modelled as an unknown deterministic parameter or as a random variable depending on the application at hand.
A Bayesian network represents conditional dependencies between random variables with a directed acyclic graph.
A modern way to model the petrophysical dependence structure between random variables is using copulas.
The influences of distribution types and linear correlation between random variables are studied.
In general, there is confusion between random variables and parameters.
The stochastic inequality is the most stringent inequality between random variables.
Bayesian networks are directed acyclic graphs for representing probabilistic relationships between random variables [ 19].
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