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In probability theory and statistics, a collection of random variables is independent and identically distributed if each random variable has the same probability distribution as the others and all are mutually independent.This property is usually abbreviated as i.i.d. or iid or IID. Herein, i.i.d. is used, because it is the most prevalent.
In statistics, it is commonly assumed that observations in a sample are effectively i.i.d. The assumption (or requirement) that observations be i.i.d. tends to simplify the underlying mathematics of many statistical methods (see mathematical statistics and statistical theory). In practical applications of statistical modeling, however, the assumption may or may not be realistic.To partially test how realistic the assumption is on a given data set, the correlation can be computed, lag plots drawn or turning point test performed. The generalization of exchangeable random variables is often sufficient and more easily met.
The i.i.d. assumption is important in the classical form of the central limit theorem, which states that the probability distribution of the sum (or average) of i.i.d. variables with finite variance approaches a normal distribution.
Often the i.i.d. assumption arises in the context of sequences of random variables. Then "independent and identically distributed" implies that an element in the sequence is independent of the random variables that came before it. In this way, an i.i.d. sequence is different from a Markov sequence, where the probability distribution for the nth random variable is a function of the previous random variable in the sequence (for a first order Markov sequence). An i.i.d. sequence does not imply the probabilities for all elements of the sample space or event space must be the same.For example, repeated throws of loaded dice will produce a sequence that is i.i.d., despite the outcomes being biased.
Suppose that the random variables and are defined to assume values in . Let and be the cumulative distribution functions of and , respectively, and denote their joint cumulative distribution function by .
Two random variables and are identically distributed if and only if .
Two random variables and are independent if and only if . (See further Independence (probability theory) § Two random variables.)
Two random variables and are i.i.d. if they are independent and identically distributed, i.e. if and only if
The definition extends naturally to more than two random variables. We say that random variables are i.i.d. if they are independent (see further Independence (probability theory)#More than two random variables) and identically distributed, i.e. if and only if
where denotes the joint cumulative distribution function of .
The following are examples or applications of i.i.d. random variables:
The following are examples of data sampling that do not satisfy the i.i.d. assumption:
Many results that were first proven under the assumption that the random variables are i.i.d. have been shown to be true even under a weaker distributional assumption.
The most general notion which shares the main properties of i.i.d. variables are exchangeable random variables, introduced by Bruno de Finetti.[ citation needed ] Exchangeability means that while variables may not be independent, future ones behave like past ones – formally, any value of a finite sequence is as likely as any permutation of those values – the joint probability distribution is invariant under the symmetric group.
This provides a useful generalization – for example, sampling without replacement is not independent, but is exchangeable.
In stochastic calculus, i.i.d. variables are thought of as a discrete time Lévy process: each variable gives how much one changes from one time to another. For example, a sequence of Bernoulli trials is interpreted as the Bernoulli process. One may generalize this to include continuous time Lévy processes, and many Lévy processes can be seen as limits of i.i.d. variables—for instance, the Wiener process is the limit of the Bernoulli process.
In machine learning theory, i.i.d. assumption is often made for training datasets to imply that all samples stem from the same generative process and that the generative process is assumed to have no memory of past generated samples.
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