https://doi.org/10.1351/goldbook.D01815
A normalized function giving the relative amount of a portion of a @[email protected] substance with a specific value, or a range of values, of a random @[email protected] or variables.
Notes:
- Distribution functions may be discrete, i.e. take on only certain specified values of the random @[email protected](s), or continuous, i.e. take on any intermediate value of the random @[email protected](s), in a given range. Most distributions in polymer science are intrinsically discrete, but it is often convenient to regard them as continuous or to use distribution functions that are inherently continuous.
- Distribution functions may be integral (or cumulative), i.e. give the proportion of the population for which a random @[email protected] is less than or equal to a given value. Alternatively they may be differential distribution functions (or @[email protected] functions), i.e. give the (maybe infinitesimal) proportion of the population for which the random @[email protected](s) is (are) within a (maybe infinitesimal) interval of its (their) range(s).
- @[email protected] requires that: (i) for a discrete differential distribution function, the sum of the function values over all possible values of the random @V[email protected](s) be unity; (ii) for a continuous differential distribution function, the integral over the entire range of the random @[email protected](s) be unity; (iii) for an integral (cumulative) distribution function, the function value at the upper limit of the random @[email protected](s) be unity.