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Suppose that ({ X_{n},ngeq1}) is coordinatewise weakly upper bounded by a random vector X.
Let ({X_{n}, ngeq1}) be a sequence of random variables weakly upper bounded by a random variable X.
If ({X_{n}, ngeq1}) is coordinatewise weakly upper bounded by a random vector X, then (3.1) implies (2.4).
If ({X_{n}, ngeq1}) is coordinatewise weakly bounded by a random vector X satisfying (2.11) and (2.12), then (3.1) holds.
Let ({X_{n}, ngeq 1}) be a sequence of (mathbb{R}^{d} -valued randomathbb{R}^{d} -valuedrandomded by a random vectorsX, and let (r>0).
Suppose that ({X_{n}, ngeq1}) is coordinatewise weakly bounded by a random vector X with sum_{j=1}^{infty}E bigl( biglvert X^{(j)} bigrvert ^{r}I bigl( biglvert X^{(j)} bigrvert leq1 bigr) bigr)< infty.
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Also, X n (r)≤1 for all r, and so |X n (r)| is bounded by an integrable random variable.
More precisely, our error estimate has an a priori and an a posteriori aspect: With a priori overwhelming probability, the (random) prefactor can be bounded by a constant that is computable without much further effort, on the basis of the given realization in the box of size $L$.
Moreover, we proposed an average-case linear time algorithm, under the assumption that the number of indels is bounded by a constant and the database structures follow the random-walk model.
Each detected bout is bounded by a red box.
One edge is bounded by a native hedge.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
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CEO of Professional Science Editing for Scientists @ prosciediting.com