= Continuous probability distribution
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A real <probability distribution> with a continuous <cumulative distribution function> assigns probability zero to each individual value. Here this is the broad meaning of <continuous probability distribution>. Many elementary examples are more specifically <absolutely continuous probability distributions>, described by a <probability density function> with respect to <Lebesgue measure>. Some texts use "continuous" for that narrower class. The <Cantor distribution> shows why the distinction matters: its <cumulative distribution function> is continuous, yet no <probability density function> with respect to <Lebesgue measure> represents its whole law. https://ocw.mit.edu/courses/6-436j-fundamentals-of-probability-fall-2018/9797310bed4c7f5b5d40d007783eec8d_MIT6_436JF18_lec04.pdf[MIT Lecture 4: continuous CDFs and the singular Cantor example]{ref}.
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