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Jul 24, 2021 at 18:18 comment added Yuval Filmus Both are random variables. If you take a random variable and pass it through a function, you get another random variable.
Jul 24, 2021 at 17:49 comment added Emad @YuvalFilmus So $X$ is a random variable? Or $T(X)$ is? There should be some random variable.
Jul 24, 2021 at 17:41 comment added Yuval Filmus It stands for the running time of the algorithm on the input $X$.
Jul 24, 2021 at 17:14 comment added Emad @YuvalFilmus Can you please explain in the definition of average-case time complexity what the expression on the right means? I know it's the mathematical expectation of some random variable but what is that variable? what does $T(X)$ mean?
Mar 28, 2017 at 12:18 vote accept hengxin
Mar 28, 2017 at 12:17 comment added Yuval Filmus Right, that's exactly what is happening there.
Mar 28, 2017 at 12:16 comment added hengxin I think the equality $$ \newcommand{\Tavg}{T_{\mathit{avg}}} \newcommand{\EE}{\mathbb{E}} \Tavg(n) = \sideset{\EE}{}{}_{X \sim \mu_n} [T(X)] = \sum_{i=0}^{n-1} \Pr[I=i] \EE[T(X)|I=i] $$ solves my confusion. It is "computing expectation by conditioning". Isn't it? Thanks.
Mar 28, 2017 at 11:50 history edited Yuval Filmus CC BY-SA 3.0
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Mar 28, 2017 at 11:45 comment added Yuval Filmus This formula is true but unhelpful. I'll update my answer with some more information.
Mar 28, 2017 at 11:40 comment added hengxin Thanks. However, I am still confused about the definitions. According to $T_{avg}(n) = E_{X \sim \mu_{n}} [T(X)]$, I would expect $T_{avg}(n) = \frac{1}{n!} \sum_{X \in U_n}T(X)$ given that $\mu_{n}$ is chosen as the uniform distribution over $U_{n}$, the set of all possible permutations. What is wrong here?
Mar 28, 2017 at 10:19 history answered Yuval Filmus CC BY-SA 3.0