Continuous probability distribution
From Wikipedia, the free encyclopedia
In probability theory, a probability distribution is called continuous if its cumulative distribution function is continuous. This is equivalent to saying that for random variables X with the distribution in question, Pr[X = a] = 0 for all real numbers a, i.e.: the probability that X attains the value a is zero, for any number a. If the distribution of X is continuous then X is called a continuous random variable.
While for a discrete probability distribution an event with probability zero is impossible (e.g. rolling 3.5 on a standard die is impossible, and has probability zero), this is not true in the case of a continuous random variable. For example, if one measures the width of an oak leaf, the result 3.5 cm is possible, but has probability zero because there are infinitely many possible values even between 3cm and 4cm. Each of these individual outcomes has probability zero, yet the probability that the outcome will fall into that interval is nonzero. This apparent paradox is resolved by the fact that the probability that X attains some value within an infinite set, such as an interval, cannot be found by naively adding the probabilities for individual values. Formally, each value has an infinitesimally small probability, which statistically is equivalent to zero.
Under an alternative and stronger definition, the term "continuous probability distribution" is reserved for distributions that have probability density functions. These are most precisely called absolutely continuous random variables (see Radon–Nikodym theorem). For a random variable X, being absolutely continuous is equivalent to saying that the probability that X attains a value in any given subset S of its range with Lebesgue measure zero is equal to zero. This does not follow from the condition Pr[X = a] = 0 for all real numbers a, since there are uncountable sets with Lebesgue-measure zero (e.g. the Cantor set).
A random variable with the Cantor distribution is continuous according to the first convention, but according to the second, it is not (absolutely) continuous. Also, it is not discrete nor a weighted average of discrete and absolutely continuous random variables.
In practical applications, random variables are often either discrete or absolutely continuous, although mixtures of the two also arise naturally.
The normal distribution, continuous uniform distribution, Beta distribution, and Gamma distribution are well known absolutely continuous distributions. The normal distribution, also called the Gaussian or the bell curve, is ubiquitous in nature and statistics due to the central limit theorem: every variable that can be modelled as a sum of many small independent variables is approximately normal.
[edit] References
 |
This article does not cite any references or sources. Please help improve this article by adding citations to reliable sources (ideally, using inline citations). Unsourced material may be challenged and removed. (November 2006) |
[edit] External links
- Continuous Random Variables. John Appleby, School of Mathematical Sciences, Dublin City University.
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||||
