Heavy-Tailed Probability Distribution
A Heavy-Tailed Probability Distribution is a probability distribution family whose tails are not exponentially bounded.
- Context:
- It can range from being Fat-Tailed Distributions, to being a Long-Tailed Distribution to being a Subexponential Distributions.
- It can be used to represent a Heavy-Tailed Dataset.
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- Counter-Example(s):
- See: Power-Law Distribution, Probability Distribution, Moment (Mathematics), Variance, Log-Normal.
References
2016
- (Wikipedia, 2016) ⇒ https://en.wikipedia.org/wiki/heavy-tailed_distribution Retrieved:2016-9-11.
- In probability theory, heavy-tailed distributions are probability distributions whose tails are not exponentially bounded: that is, they have heavier tails than the exponential distribution. In many applications it is the right tail of the distribution that is of interest, but a distribution may have a heavy left tail, or both tails may be heavy.
There are three important subclasses of heavy-tailed distributions: the fat-tailed distributions, the long-tailed distributions and the subexponential distributions. In practice, all commonly used heavy-tailed distributions belong to the subexponential class.
There is still some discrepancy over the use of the term heavy-tailed. There are two other definitions in use. Some authors use the term to refer to those distributions which do not have all their power moments finite; and some others to those distributions that do not have a finite variance. The definition given in this article is the most general in use, and includes all distributions encompassed by the alternative definitions, as well as those distributions such as log-normal that possess all their power moments, yet which are generally acknowledged to be heavy-tailed. (Occasionally, heavy-tailed is used for any distribution that has heavier tails than the normal distribution.)
- In probability theory, heavy-tailed distributions are probability distributions whose tails are not exponentially bounded: that is, they have heavier tails than the exponential distribution. In many applications it is the right tail of the distribution that is of interest, but a distribution may have a heavy left tail, or both tails may be heavy.