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Probability theory 

In probability theory, two events are independent, statistically independent, or stochastically independent^{[1]} if the occurrence of one does not affect the probability of occurrence of the other (equivalently, does not affect the odds). Similarly, two random variables are independent if the realization of one does not affect the probability distribution of the other.
The concept of independence extends to dealing with collections of more than two events or random variables, in which case the events are pairwise independent if each pair are independent of each other, and the events are mutually independent if each event is independent of each other combination of events.
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Contents
Definition
For events
Two events
Two events and are independent (often written as or ) if and only if their joint probability equals the product of their probabilities:^{[2]}^{:p. 29}^{[3]}^{:p. 10}


(Eq.1) 
Why this defines independence is made clear by rewriting with conditional probabilities:
 .
and similarly
 .
Thus, the occurrence of does not affect the probability of , and vice versa. Although the derived expressions may seem more intuitive, they are not the preferred definition, as the conditional probabilities may be undefined if or are 0. Furthermore, the preferred definition makes clear by symmetry that when is independent of , is also independent of .
Log probability and information content
Stated in terms of log probability, two events are independent if and only if the log probability of the joint event is the sum of the log probability of the individual events:
In information theory, negative log probability is interpreted as information content, and thus two events are independent if and only if the information content of the combined event equals the sum of information content of the individual events:
See Information content § Additivity of independent events for details.
Odds
Stated in terms of odds, two events are independent if and only if the odds ratio of and is unity (1). Analogously with probability, this is equivalent to the conditional odds being equal to the unconditional odds:
or to the odds of one event, given the other event, being the same as the odds of the event, given the other event not occurring:
The odds ratio can be defined as
or symmetrically for odds of given , and thus is 1 if and only if the events are independent.
More than two events
A finite set of events is pairwise independent if every pair of events is independent^{[4]}—that is, if and only if for all distinct pairs of indices ,


(Eq.2) 
A finite set of events is mutually independent if every event is independent of any intersection of the other events^{[4]}^{[3]}^{:p. 11}—that is, if and only if for every and for every element subset of events of ,


(Eq.3) 
This is called the multiplication rule for independent events. Note that it is not a single condition involving only the product of all the probabilities of all single events (see below for a counterexample); it must hold true for all subsets of events.
For more than two events, a mutually independent set of events is (by definition) pairwise independent; but the converse is not necessarily true (see below for a counterexample).^{[2]}^{:p. 30}
For real valued random variables
Two random variables
Two random variables and are independent if and only if (iff) the elements of the πsystem generated by them are independent; that is to say, for every and , the events and are independent events (as defined above in Eq.1). That is, and with cumulative distribution functions and , are independent iff the combined random variable has a joint cumulative distribution function^{[3]}^{:p. 15}


(Eq.4) 
or equivalently, if the probability densities and and the joint probability density exist,
 .
More than two random variables
A finite set of random variables is pairwise independent if and only if every pair of random variables is independent. Even if the set of random variables is pairwise independent, it is not necessarily mutually independent as defined next.
A finite set of random variables is mutually independent if and only if for any sequence of numbers , the events are mutually independent events (as defined above in Eq.3). This is equivalent to the following condition on the joint cumulative distribution function . A finite set of random variables is mutually independent if and only if^{[3]}^{:p. 16}


(Eq.5) 
Notice that is not necessary here to require that the probability distribution factorizes for all possible element subsets as in the case for events. This is not required because e.g. implies .
The measuretheoretically inclined may prefer to substitute events for events in the above definition, where is any Borel set. That definition is exactly equivalent to the one above when the values of the random variables are real numbers. It has the advantage of working also for complexvalued random variables or for random variables taking values in any measurable space (which includes topological spaces endowed by appropriate σalgebras).
For real valued random vectors
Two random vectors and are called independent if^{[5]}^{:p. 187}


(Eq.6) 
where and denote the cumulative distribution functions of and and denotes their joint cumulative distribution function. Independence of and is often denoted by . Written componentwise, and are called independent if
 .
For stochastic processes
For one stochastic process
The definition of independence may be extended from random vectors to a stochastic process. Thereby it is required for an independent stochastic process that the random variables obtained by sampling the process at any times are independent random variables for any .^{[6]}^{:p. 163}
Formally, a stochastic process is called independent, if and only if for all and for all


(Eq.7) 
where . Notice that independence of a stochastic process is a property within a stochastic process, not between two stochastic processes.
For two stochastic processes
Independence of two stochastic processes is a property between two stochastic processes and that are defined on the same probability space . Formally, two stochastic processes and are said to be independent if for all and for all , the random vectors and are independent,^{[7]}^{:p. 515} i.e. if


(Eq.8) 
Independent σalgebras
The definitions above (Eq.1 and Eq.2) are both generalized by the following definition of independence for σalgebras. Let be a probability space and let and be two subσalgebras of . and are said to be independent if, whenever and ,
Likewise, a finite family of σalgebras , where is an index set, is said to be independent if and only if
and an infinite family of σalgebras is said to be independent if all its finite subfamilies are independent.
The new definition relates to the previous ones very directly:
 Two events are independent (in the old sense) if and only if the σalgebras that they generate are independent (in the new sense). The σalgebra generated by an event is, by definition,
 Two random variables and defined over are independent (in the old sense) if and only if the σalgebras that they generate are independent (in the new sense). The σalgebra generated by a random variable taking values in some measurable space consists, by definition, of all subsets of of the form , where is any measurable subset of .
Using this definition, it is easy to show that if and are random variables and is constant, then and are independent, since the σalgebra generated by a constant random variable is the trivial σalgebra . Probability zero events cannot affect independence so independence also holds if is only Pralmost surely constant.
Properties
Selfindependence
Note that an event is independent of itself if and only if
 .
Thus an event is independent of itself if and only if it almost surely occurs or its complement almost surely occurs; this fact is useful when proving zero–one laws.^{[8]}
Expectation and covariance
If and are independent random variables, then the expectation operator has the property
and the covariance is zero, since we have
 .
(The converse of these, i.e. the proposition that if two random variables have a covariance of 0 they must be independent, is not true. See uncorrelated.)
Similarly for two stochastic processes and : If they are independent, then they are uncorrelated.^{[9]}^{:p. 151}
Characteristic function
Two random variables and are independent if and only if the characteristic function of the random vector satisfies
 .
In particular the characteristic function of their sum is the product of their marginal characteristic functions:
though the reverse implication is not true. Random variables that satisfy the latter condition are called subindependent.
Examples
Rolling dice
The event of getting a 6 the first time a die is rolled and the event of getting a 6 the second time are independent. By contrast, the event of getting a 6 the first time a die is rolled and the event that the sum of the numbers seen on the first and second trial is 8 are not independent.
Drawing cards
If two cards are drawn with replacement from a deck of cards, the event of drawing a red card on the first trial and that of drawing a red card on the second trial are independent. By contrast, if two cards are drawn without replacement from a deck of cards, the event of drawing a red card on the first trial and that of drawing a red card on the second trial are not independent, because a deck that has had a red card removed has proportionately fewer red cards.
Pairwise and mutual independence
Consider the two probability spaces shown. In both cases, and . The random variables in the first space are pairwise independent because , , and ; but the three random variables are not mutually independent. The random variables in the second space are both pairwise independent and mutually independent. To illustrate the difference, consider conditioning on two events. In the pairwise independent case, although any one event is independent of each of the other two individually, it is not independent of the intersection of the other two:
In the mutually independent case, however,
Mutual independence
It is possible to create a threeevent example in which
and yet no two of the three events are pairwise independent (and hence the set of events are not mutually independent).^{[10]} This example shows that mutual independence involves requirements on the products of probabilities of all combinations of events, not just the single events as in this example. For another example, take to be empty and and to be identical events with nonzero probability. Then, since and are the same event, they are not independent, but the probability of the intersection of the events is zero, the product of the probabilities.
Conditional independence
For events
The events and are conditionally independent given an event when
.
For random variables
Intuitively, two random variables and are conditionally independent given if, once is known, the value of does not add any additional information about . For instance, two measurements and of the same underlying quantity are not independent, but they are conditionally independent given (unless the errors in the two measurements are somehow connected).
The formal definition of conditional independence is based on the idea of conditional distributions. If , , and are discrete random variables, then we define and to be conditionally independent given if
for all , and such that . On the other hand, if the random variables are continuous and have a joint probability density function , then and are conditionally independent given if
for all real numbers , and such that .
If discrete and are conditionally independent given , then
for any , and with . That is, the conditional distribution for given and is the same as that given alone. A similar equation holds for the conditional probability density functions in the continuous case.
Independence can be seen as a special kind of conditional independence, since probability can be seen as a kind of conditional probability given no events.
See also
 Copula (statistics)
 Independent and identically distributed random variables
 Mutually exclusive events
 Subindependence
 Conditional independence
 Normally distributed and uncorrelated does not imply independent
 Mean dependence
References
 ^ Russell, Stuart; Norvig, Peter (2002). Artificial Intelligence: A Modern Approach. Prentice Hall. p. 478. ISBN 0137903952.
 ^ ^{a} ^{b} Florescu, Ionut (2014). Probability and Stochastic Processes. Wiley. ISBN 9780470624555.
 ^ ^{a} ^{b} ^{c} ^{d} Gallager, Robert G. (2013). Stochastic Processes Theory for Applications. Cambridge University Press. ISBN 9781107039759.
 ^ ^{a} ^{b} Feller, W (1971). "Stochastic Independence". An Introduction to Probability Theory and Its Applications. Wiley.
 ^ Papoulis, Athanasios (1991). Probability, Random Variables and Stochastic Porcesses. MCGraw Hill. ISBN 0070484775.
 ^ Hwei, Piao (1997). Theory and Problems of Probability, Random Variables, and Random Processes. McGrawHill. ISBN 0070306443.
 ^ Amos Lapidoth (8 February 2017). A Foundation in Digital Communication. Cambridge University Press. ISBN 9781107177321.
 ^ Durrett, Richard (1996). Probability: theory and examples (Second ed.). page 62
 ^ Park,Kun Il (2018). Fundamentals of Probability and Stochastic Processes with Applications to Communications. Springer. ISBN 9783319680743.
 ^ George, Glyn, "Testing for the independence of three events," Mathematical Gazette 88, November 2004, 568. PDF