Mathematically, you can bring discrete and continuous distributions on a common
footing by defining probability functions as densities wrt. counting measure.
You don't really need Radon-Nikodym derivatives to understand the idea, just
the fact that sums can be interpreted as integrals wrt counting measure, hence
sum_{x in A} f(x) and int_A f(x) dx are essentially the same concept.
-pd
> On 15 Mar 2019, at 01:43 , Stefan Schreiber <[email protected]> wrote:
>
> Dear R users,
>
> While experimenting with the dbinom() function and reading its
> documentation (?dbinom) it reads that "dbinom gives the density" but
> shouldn't it be called "mass" instead of "density"? I assume that it
> has something to do with keeping the function for "density" consistent
> across discrete and continuous probability functions - but I am not
> sure and was hoping someone could clarify?
>
> Furthermore the help file for dbinom() function references a link
> (http://www.herine.net/stat/software/dbinom.html) but it doesn't seem
> to land where it should. Maybe this could be updated?
>
> Thank you,
> Stefan
>
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--
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Office: A 4.23
Email: [email protected] Priv: [email protected]
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