# Independent functions, system of

A sequence of measurable functions $\{ f _ {i} \}$ such that

$$\mu \{ {x } : {f _ {1} ( x) < \alpha _ {1} \dots f _ {n} ( x) < \alpha _ {n} } \} = \ \prod _ { i= } 1 ^ { n } \mu \{ {x } : {f _ {i} ( x) < \alpha _ {i} } \}$$

for any $n$ and any $\alpha _ {1} \dots \alpha _ {n}$. The simplest example of a system of independent functions is the Rademacher system.

(Kolmogorov's) criterion for the almost-everywhere convergence of a series of independent functions: For a series of independent functions $\sum _ {i=} 1 ^ \infty f _ {i}$ to converge almost everywhere it is necessary and sufficient that for some $C > 0$ the following three series converge:

$$\sum _ { i } \mu \{ {x } : {f _ {i} ( x) > C } \} ,\ \ \sum _ { i } \int\limits f _ {i} ^ { C } ( x) d x ,$$

$$\sum _ { i } \int\limits ( f _ {i} ^ { C } ( x) ) ^ {2} \ d x - \left ( \int\limits f _ {i} ^ { C } ( x) d x \right ) ^ {2} ,$$

where

$$f _ {i} ^ { C } ( x) = \ \left \{ \begin{array}{ll} f _ {i} ( x) , & | f _ {i} ( x) | \leq C , \\ 0 , & | f _ {i} ( x) | > C . \\ \end{array} \right .$$

Of course, to be able to introduce the concept of a system of independent functions one needs to have a measure space $( X , \mu )$ on which the functions are defined and measurable (with respect to $\mu$). Moreover, $\mu$ must be positive and finite, so $\mu$ can be taken a probability measure (then $( X , \mu )$ is a probability space). An example is $( X , \mu ) = ( [ 0 , 1 ], \textrm{ Lebesgue measure } )$.
The notion of a system of independent functions (random variables) should not be mixed up with that of an independent set of elements of a vector space $V$ over a field $K$: A set of elements $\{ x _ {1} \dots x _ {n} \}$ in $V$ such that for $c _ {i} \in K$, $c _ {1} x _ {1} + \dots + c _ {n} x _ {n} = 0$ implies $c _ {1} = \dots = c _ {n} = 0$, see also Vector space.