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It is simple to derive a rough relationship between the mass and radii of white dwarfs using an energy minimization argument.
It is simple to derive the average response at the time of the reward (t = N) in trial T, i.e., the average TD error δ T (N), from the TD learning rule with the simplified tapped delay-line time representation and a fixed learning rate α.
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An explicit form of F is simple to derive by elementary transformations taking into account (4.5) and the properties of the Cauchy matrix.
The variance (due to the model) of this estimator is simpler to derive, since it does not involve any residual terms; thus uncertainty in this case is introduced only through the model parameter estimation.
It is simple yet effective in deriving less conservative exponential convergence conditions and more detailed componentwise decay estimates.
If a demand for parsimony matters, it is simpler and more elegant to derive matter form mind than vice versa.
Therefore, we see that it is important to derive simple expressions for the outage probability which will be helpful in achieving more insights about the system behavior.
Therefore, we see that it is important to derive simple expressions for the outage probability and ASEP at the high SNR values where more insights about the system behavior can be achieved.
For frequency-selective fading, and (14) will be evaluated via Monte Carlo simulation since it is difficult to derive a simple closed form expression.
In simple cases it is possible to derive an explicit analytical expression of this index, which allows one to compare with simulations, and get a sense of how it transfers to the concrete finite case.
It is possible to derive a simple expression status (expressed or not expressed) for conditions or tissues, for which multiple data-sets are available.
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