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 <sschr...@ualberta.ca> 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 > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. -- Peter Dalgaard, Professor, Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Office: A 4.23 Email: pd....@cbs.dk Priv: pda...@gmail.com ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.