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It is clearly motivated by equation (1), which suffices for finite sample spaces because there are only finitely many events.
Because of their comparative simplicity, experiments with finite sample spaces are discussed first.
In other words, there is a sequence of experiments, but each one involves a finite sample space.
Moreover, we obtain asymptotic and finite sample theorems.
Let $h$ be the hypothesis of interest and, for the sake of simplicity, let $S$ be a finite sample space.
The typical size scales of the fibrils give rise to finite sample thickness effects [16, 36].
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JD+ obtains exact finite-sample measures from state space algorithms.
Section 3.2 then discusses the precoding design to combat the effect of finite-sample estimation errors.
This equality testing employs bootstrap resampling to obtain the finite-sample distribution of the statistics.
From these we produce analytic finite-sample approximations and demonstrate their accuracy via numerical examples.
This new method can be assimilated to a finite-sample Wiener Kolmogorov framework.
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