Random variable
Variable representing a random phenomenon
Concept
A random variable (also called random quantity, aleatory variable, or stochastic variable) is a mathematical formalization of a quantity or object which depends on random events. The term 'random variable' in its mathematical definition refers to neither randomness nor variability but instead is a mathematical function in which the domain is the set of possible outcomes in a sample space (e.g. the set { H , T } {\displaystyle \{H,T\}} (which are the possible upper sides of a flipped coin heads H {\displaystyle H} or tails T {\displaystyle T} as the result from tossing a coin); and the range is a measurable space (e.g. corresponding to the domain above, the range might be the set { − 1 , 1 } {\displaystyle \{-1,1\}} if say heads H {\displaystyle H} mapped to −1 and T {\displaystyle T} mapped to 1). Typically, the range of a random variable is a subset of the real numbers. Informally, randomness typically represents some fundamental element of chance, such as in the roll of a die; it may also represent uncertainty, such as measurement error. However, the interpretation of probability is philosophically complicated, and even in specific cases is not always straightforward. The purely mathematical analysis of random variables is independent of such interpretational difficulties, and can be based upon a rigorous axiomatic setup. In the formal mathematical language of measure theory, a random variable is defined as a measurable function from a probability measure space (called the sample space) to a measurable space. This allows consideration of the pushforward measure, which is called the distribution of the random variable; the distribution is thus a probability measure on the set of all possible values of the random variable. It is possible for two random variables to have identical distributions but to differ in significant ways; for instance, they may be independent. It is common to consider the special cases of discrete random variables and absolutely continuous random variables, corresponding to whether a random variable is valued in a countable subset or in an interval of real numbers. There are other important possibilities, especially in the theory of stochastic processes, wherein it is natural to consider random sequences or random functions. Sometimes a random variable is taken to be automatically valued in the real numbers, with more general random quantities instead being called random elements. A random variate is a particular outcome or realization of a random variable. According to George Mackey, Pafnuty Chebyshev was the first person "to think systematically in terms of random variables".
CardsKnowledge
Random variable
Variable representing a random phenomenon
A random variable (also called random quantity, aleatory variable, or stochastic variable) is a mathematical formalization of a quantity or object which depends on random events. The term 'random variable' in its mathematical definition refers to neither randomness nor variability but instead is a mathematical function in which the domain is the set of possible outcomes in a sample space (e.g. the set { H , T } {\displaystyle \{H,T\}} (which are the possible upper sides of a flipped coin heads H {\displaystyle H} or tails T {\displaystyle T} as the result from tossing a coin); and the range is a measurable space (e.g. corresponding to the domain above, the range might be the set { − 1 , 1 } {\displaystyle \{-1,1\}} if say heads H {\displaystyle H} mapped to −1 and T {\displaystyle T} mapped to 1). Typically, the range of a random variable is a subset of the real numbers. Informally, randomness typically represents some fundamental element of chance, such as in the roll of a die; it may also represent uncertainty, such as measurement error. However, the interpretation of probability is philosophically complicated, and even in specific cases is not always straightforward. The purely mathematical analysis of random variables is independent of such interpretational difficulties, and can be based upon a rigorous axiomatic setup. In the formal mathematical language of measure theory, a random variable is defined as a measurable function from a probability measure space (called the sample space) to a measurable space. This allows consideration of the pushforward measure, which is called the distribution of the random variable; the distribution is thus a probability measure on the set of all possible values of the random variable. It is possible for two random variables to have identical distributions but to differ in significant ways; for instance, they may be independent. It is common to consider the special cases of discrete random variables and absolutely continuous random variables, corresponding to whether a random variable is valued in a countable subset or in an interval of real numbers. There are other important possibilities, especially in the theory of stochastic processes, wherein it is natural to consider random sequences or random functions. Sometimes a random variable is taken to be automatically valued in the real numbers, with more general random quantities instead being called random elements. A random variate is a particular outcome or realization of a random variable. According to George Mackey, Pafnuty Chebyshev was the first person "to think systematically in terms of random variables".
Read the article on Wikipedia ↗How these stats are calculated
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Concept — Alters a rule of the game for as long as it stays in play.
Reach
Article length
85 / 120
Rigour
Reference density and count
48 / 120
Anchorage
Age and revision count
328 / 400
Currency
Recent edit activity and view trend
7 / 30
Diffusion
Language editions and incoming links
73 / 100
Article size
43 kB
Sources cited
15
Sections
27
Revisions
1,280
Revisions in 90 days
1
Incoming links
1,266
Language editions
66
Views over 12 months
126,904
Created on
February 23, 2001
Wikidata statements
49
Rare — 98.7th percentile of notability
Rarity ranks cards by how famous their subject is: readership, number of languages and incoming links. This tier appears in 10.00% of cards pulled from packs. It says nothing about the card’s power, which is 586 out of 1000 here.
Effect
This card does not fight on its own: it carries a single effect, chosen by its dominant measure.
Knowledge — type matchups
Strong against
Weak against
The source article
This card maps to the Wikidata item Q176623, present in 66 Wikipedia editions.
Illustration : file on Wikimedia Commons — Ainali — licence CC BY-SA 3.0. Article text licensed under CC BY-SA 4.0.
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