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In D AI one would be taking expectations of the log-likelihood ratio over all possible realizations expected if the 'full model' (association) were true; on the other hand, in D IA the expectations of the reciprocal of the log-likelihood would be taken under the assumption of independence.
Taking expectations on both sides, we get (D10).
By multiplying both sides of (12) by and taking expectations, we obtain (13).
Taking expectations on both sides of (3.11) and noticing that (3.16).
Assuming equiprobable symbols and taking expectations on both sides yields: E{hat{omega}_{i}(t)} = omega_{c}.
In particular, the following two difference equations, which are obtained by just taking expectations on both sides of Eqs.
Similar(28)
By taking expectation of packet reception and loss cases in the first product in (9), we can simplify expression (9) as: (10).
Integrating both sides for the above inequality from 0 to (tau_{varepsilon}wedge T) and taking expectation, we obtain EVbigl(S tau_{varepsilon}wedge T),x tau_{varepsilon}wedge T bigr)leq V(S_{0},x_{0})+C_{2}T.
Indeed, two key challenges encountered in the analysis of ergodic capacity include obtaining the exact PDF of the end-to-end SIR and taking expectation of the nonlinear log function.
end{aligned} Thus, it suffices to replace t by the random variable T and then to take expectations.
xi minus mu square, when you take expectation, that will be sigma square.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com