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The modern proof of the strong law is more complex than that of the weak law, and relies on passing to an appropriate subsequence. [17] The strong law of large numbers can itself be seen as a special case of the pointwise ergodic theorem. This view justifies the intuitive interpretation of the expected value (for Lebesgue integration only) of a ...
Kronecker's lemma. In mathematics, Kronecker's lemma (see, e.g., Shiryaev (1996, Lemma IV.3.2)) is a result about the relationship between convergence of infinite sums and convergence of sequences. The lemma is often used in the proofs of theorems concerning sums of independent random variables such as the strong Law of large numbers.
For every (fixed) , is a sequence of random variables which converge to () almost surely by the strong law of large numbers. Glivenko and Cantelli strengthened this result by proving uniform convergence of F n {\displaystyle \ F_{n}\ } to F . {\displaystyle \ F~.}
The law of truly large numbers (a statistical adage), attributed to Persi Diaconis and Frederick Mosteller, states that with a large enough number of independent samples, any highly implausible (i.e. unlikely in any single sample, but with constant probability strictly greater than 0 in any sample) result is likely to be observed. [1]
It is the notion of convergence used in the strong law of large numbers. The concept of almost sure convergence does not come from a topology on the space of random variables. This means there is no topology on the space of random variables such that the almost surely convergent sequences are exactly the converging sequences with respect to ...
Toggle Proof subsection. 2.1 Sufficiency of conditions ("if") ... can be used to give a relatively easy proof of the Strong Law of Large Numbers. [1]
The law of the iterated logarithm specifies what is happening "in between" the law of large numbers and the central limit theorem. Specifically it says that the normalizing function √ n log log n, intermediate in size between n of the law of large numbers and √ n of the central limit theorem, provides a non-trivial limiting behavior.
A renewal process has asymptotic properties analogous to the strong law of large numbers and central limit theorem. The renewal function () (expected number of arrivals) and reward function () (expected reward value) are of key importance in renewal theory. The renewal function satisfies a recursive integral equation, the renewal equation.