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A quick review of resolving expectations: If you know that a discrete random variable $X$, drawn from set $\mathcal{X}$ has probability distribution $p(x) = \mathbf{Pr}\{X=x \}$, then $$\mathbb{E}[X] = \sum_{x \in \mathcal{X}} xp(x)$$ This equation is the core of what is going on when resolving the expectation in your quoted equation. Resolving the ...


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Independent and identically distributed random variables share the same probability distribution and each item doesn’t influence or provide insight about the value of the next item you measure. The most common example is a coin toss: as you flip the coin, one outcome does not influence or predict the next one. As for a dataset of flowers, we assume that the ...


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