Free independence
In the mathematical theory of free probability, the notion of free independence was introduced by Dan Voiculescu.[1] The definition of free independence is parallel to the classical definition of independence, except that the role of Cartesian products of measure spaces (corresponding to tensor products of their function algebras) is played by the notion of a free product of (non-commutative) probability spaces.
In the context of Voiculescu's free probability theory, many classical-probability theorems or phenomena have free probability analogs: the same theorem or phenomenon holds (perhaps with slight modifications) if the classical notion of independence is replaced by free independence. Examples of this include: the free central limit theorem; notions of free convolution; existence of free stochastic calculus and so on.
Let 
 be a non-commutative probability space, i.e. a unital algebra 
 over 
 equipped with a unital linear functional 
.  As an example, one could take, for a probability measure 
,
Another example may be 
, the algebra of 
 matrices with the functional given by the normalized trace 
.  Even more generally, 
 could be a von Neumann algebra and 
 a state on 
.  A final example is the group algebra 
 of a (discrete) group 
 with the functional 
 given by the group trace 
.
Let 
  be a family of unital subalgebras of 
.
Definition.  The family 
  is called freely independent if 
whenever 
, 
 and 
.
If 
, 
 is a family of elements of 
 (these can be thought of as random variables in 
), they are called
freely independent  if the algebras 
 generated by 
 and 
 are freely independent.
Examples of free independence
-  Let 
 be the free product of groups 
, let 
 be the group algebra, 
 be the group trace, and set 
.  Then 
 are freely independent. -  Let 
 be 
 unitary random matrices, taken independently at random from the 
 unitary group (with respect to the Haar measure).  Then 
 become asymptotically freely independent as 
. (Asymptotic freeness means that the definition of freeness holds in the limit as 
). - More generally, independent random matrices tend to be asymptotically freely independent, under certain conditions.
 
References
- ↑ D. Voiculescu, K. Dykema, A. Nica, "Free Random Variables", CIRM Monograph Series, AMS, Providence, RI, 1992
 
