In probability and statistics, an **exponential family** is an important class of probability distributions sharing a certain form, specified below. This special form is chosen for mathematical convenience, on account of some useful algebraic properties, as well as for generality, as exponential families are in a sense very natural distributions to consider. The concept of exponential families is credited to E. J. G. Pitman, G. Darmois, and B. O. Koopman in 1935–6. The term **exponential class** is sometimes used in place of "exponential family".

The exponential families include many of the most common distributions, including the normal, exponential, gamma, chi-squared, beta, Dirichlet, Bernoulli, categorical, Poisson, Wishart, Inverse Wishart and many others. A number of common distributions are exponential families only when certain parameters are considered fixed and known, e.g. binomial (with fixed number of trials), multinomial (with fixed number of trials), and negative binomial (with fixed number of failures). Examples of common distributions that are not exponential families are Student's t, most mixture distributions, and even the family of uniform distributions with unknown bounds. See the section below on examples for more discussion.

Consideration of exponential-family distributions provides a general framework for selecting a possible alternative parameterisation of the distribution, in terms of **natural parameters**, and for defining useful sample statistics, called the **natural statistics** of the family. See below for more information.

Read more about Exponential Family: Definition, The Meaning of "exponential Family", Interpretation, Properties, Examples, Table of Distributions, Maximum Entropy Derivation

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### Famous quotes containing the word family:

“I worry about people who get born nowadays, because they get born into such tiny families—sometimes into no *family* at all. When you’re the only pea in the pod, your parents are likely to get you confused with the Hope Diamond. And that encourages you to talk too much.”

—Russell Baker (b. 1925)