handout_week2_b

# handout_week2_b - Stochastic Signals and Systems Random...

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Stochastic Signals and Systems Random Variables Virginia Tech Fall 2008 Distribution Function: Deﬁnition Let X be a random variable. The (cumulative) distribution function (cdf) (also known as probability distribution function ) F X ( x ) is deﬁned by F X ( x ) = P [ X x ] for all values x in -∞ ≤ x ≤ ∞ . That is, the cumulative distribution function F X ( x ) is the probability of the event, “The random variable X takes on a value equal to or less than x in a trial of the random experiment.” Note: We could, for example, use the notation F X ( ω ) to represent this function. Speciﬁcally, F X ( ω ) = P [ X ω ] .

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Example In the coin-tossing experiment, the probability of heads equals p and the probability of tails equals q . We deﬁne the random variable X such that X ( h ) = 1 and X ( t ) = 0. Solution: x < 0, { X x } = {∅} , so that F X ( x ) = 0. 0 x < 1, { X x } = { t } , so that F X ( x ) = P [ { t } ] = 1 - p . x
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## This note was uploaded on 05/05/2010 for the course ECECS 5605 taught by Professor Dasilver during the Fall '08 term at Virginia Tech.

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handout_week2_b - Stochastic Signals and Systems Random...

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