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I have two random variables $X$ and $Y$ and I want to bound the value of one in terms of the other (for now, I don't care about the actual distribution of their values).

Suppose that the two variables can have different distributions with values chosen from $[1, n]$. But $X$ is always upper bounded by $Y \cdot c\log{n}$ for some constant $c$. Can I write this as $X = O(Y\log{n})$ (if I care about the behavior for large $n$). I'm not sure what is the convention wrt to random variables and asymptotic notation.

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  • $\begingroup$ "$X$ is always upper bounded by $Y⋅\log n$." You write that as $X\le Y\log n$. What is the problem with that? Do you have a different relation between $X$ and $Y$? $\endgroup$ – Apass.Jack Jul 7 at 8:05
  • $\begingroup$ sorry, I forgot to add the constant for the upper bound. There exists a constant $c$ for large $n$ such that the bound holds, so I want to write this in asymptotic notation. $\endgroup$ – user1246462 Jul 7 at 15:00
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On first look, the notation $X = O(Y\,\log{n})$ seems intuitively reasonable since $X$ is always upper bounded by $c (Y\,\log n)$ for some constant $c$. ($c$ must be positive since $X,Y\ge1$).

Can we formalize the exact meaning of that notation?

Here is one natural definition.

Let $(\Omega,S,T)$ represents a set of 2-dimensional random vectors on $\Omega$ parameterized by $n$, i.e., for all $n$, $(S_n, T_n)$ is a random vector from $\Omega_n$ (which is a set of possible outcomes) to ${\Bbb R}^2$. We write

$$S=O(T){\text{ as }}n\to \infty $$ if and only if there is a positive constant $c$ such that for all sufficiently large values of $n$, for all $\omega\in\Omega_n$, the absolute value of $S_n(w)$ is at most $c\,T_n(w)$.

We can apply the above definition to $(I, X, Y\,\log n)$ where $I_n=[1,n]$.

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