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Montgolfière (talk | contribs) Mixture distributions generally do have densities |
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In many cases, <math>X</math> is [[Real number|real-valued]], i.e. <math>E = \mathbb{R}</math>. In some contexts, the term [[random element]] (see [[#Extensions|extensions]]) is used to denote a random variable not of this form.
{{Anchor|Discrete random variable}}When the [[Image (mathematics)|image]] (or range) of <math>X</math> is [[countable set|countable]], the random variable is called a '''discrete random variable'''<ref name="Yates">{{cite book | last1 = Yates | first1 = Daniel S. | last2 = Moore | first2 = David S | last3 = Starnes | first3 = Daren S. | year = 2003 | title = The Practice of Statistics | edition = 2nd | publisher = [[W. H. Freeman and Company|Freeman]] | ___location = New York | url = http://bcs.whfreeman.com/yates2e/ | isbn = 978-0-7167-4773-4 | url-status = dead | archive-url = https://web.archive.org/web/20050209001108/http://bcs.whfreeman.com/yates2e/ | archive-date = 2005-02-09 }}</ref>{{rp|399}} and its distribution is a [[discrete probability distribution]], i.e. can be described by a [[probability mass function]] that assigns a probability to each value in the image of <math>X</math>. If the image is uncountably infinite (usually an [[Interval (mathematics)|interval]]) then <math>X</math> is called a '''continuous random variable'''.<ref>{{Cite web|title=Random Variables|url=http://www.stat.yale.edu/Courses/1997-98/101/ranvar.htm|access-date=2020-08-21|website=www.stat.yale.edu}}</ref><ref>{{Cite journal|last1=Dekking|first1=Frederik Michel|last2=Kraaikamp|first2=Cornelis|last3=Lopuhaä|first3=Hendrik Paul|last4=Meester|first4=Ludolf Erwin|date=2005|title=A Modern Introduction to Probability and Statistics|url=https://doi.org/10.1007/1-84628-168-7|journal=Springer Texts in Statistics|language=en-gb|doi=10.1007/1-84628-168-7|isbn=978-1-85233-896-1|issn=1431-875X}}</ref> In the special case that it is [[absolutely continuous]], its distribution can be described by a [[probability density function]], which assigns probabilities to intervals; in particular, each individual point must necessarily have probability zero for an absolutely continuous random variable. Not all continuous random variables are absolutely continuous
Any random variable can be described by its [[cumulative distribution function]], which describes the probability that the random variable will be less than or equal to a certain value.
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