In probability and statistics, a probability distribution's quantile function is the inverse of its cumulative distribution function. That is, the quantile function of a distribution D {\displaystyle {\mathcal {D}}} is the function Q {\displaystyle Q} such that Pr [ X ≤ Q ( p ) ] = p {\displaystyle \Pr \left[\mathrm {X} \leq Q(p)\right]=p} for any random variable X ∼ D {\displaystyle \mathrm {X} \sim {\mathcal {D}}} and probability p ∈ ( 0 , 1 ) {\displaystyle p\in (0,1)} .
In probability and statistics, a probability distribution's quantile function is the inverse of its cumulative distribution function. That is, the quantile function of a distribution D {\displaystyle {\mathcal {D}}} is the function Q {\displaystyle Q} such that Pr [ X ≤ Q ( p ) ] = p {\displaystyle \Pr \left[\mathrm {X} \leq Q(p)\right]=p} for any random variable X ∼ D {\displaystyle \mathrm {X} \sim {\mathcal {D}}} and probability p ∈ ( 0 , 1 ) {\displaystyle p\in (0,1)} .
Source: Wikipedia "Quantile function" · CC BY-SA 4.0
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