In the following lesson, we only consider independent random values that are drawn from identical PDFs, often labeled as i.i.d. (independent and identically distributed) data. That is, we do not consider cases with different probabilities when given a specific value of a random variable in a previous trial. We assume, mainly for simplicity, that this static probability density function describes all that we can know about the corresponding random variable.

Let’s consider the arbitrary PDF, $p(x)$, with the following graph:

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