ECE600 F13 Statistical Independence mhossain - Rhea

Random Variables and Signals

Topic 4: Statistical Independence

Definition

People generally have an idea of the concept of independence, but we will formalize it for probability theory here.

Definition $\qquad$ Given (S,F,P) and A, B ∈ F, events A and B are statistically indendent iff

P(A\cap B) = P(A)P(B)

It may seem more intuitive if we consider that, from the definition above, we can say that A and B are statistically independent iff P(A|B)P(B) = P(A)P(B), which means P(A|B) = P(A)

Notation $\qquad$ A, B are independent ⇔

X \perp\!\!\!\perp Y

Note $\qquad$ We will usually refer to statistical independence as simply independence in this course.

Note $\qquad$ If A and B are independent, then A' and B are also independent.

Proof:

\begin{align} B&=(A\cap B)\cup(A'\cap B) \\ \Rightarrow P(B)&=p(A\cap B)+P(A'\cap B) \\ &=P(A)P(B) + P(A'\cap B) \end{align}

\begin{align} \Rightarrow P(A'\cap B) &= P(B) - P(A)P(B) \\ &= P(B)[1-P(A)] \\ &= P(B)P(A') \end{align}

Definition $\qquad$ Events $$ A_1,A_2,...,A_n $$ are statistically independent iff for any $$ k=1,2,...,n $$ , and any 1≤ $$ j_1,j_2,...,j_k $$ ≤n, for any finite n, we have that

P(A_{j_1}\cap...\cap A_{j_1}) = \prod_{i=1}^k P(A_{j_1})

References

M. Comer. ECE 600. Class Lecture. Random Variables and Signals. Faculty of Electrical Engineering, Purdue University. Fall 2013.

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ECE600 F13 Statistical Independence mhossain - Rhea

Definition

References

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