2 Conditioning, Total Probability Theorem, and Bayes' Rule
2018
Example 1: Let the conditioning event be: E =- {X > c}. Then the conditional PDF becomes: Pp(xv, X>>c)c) 0P(PX(x>) c) if x > c p(xIE) = p(x1X > c) = otherwise Similar to the definition of independence between two events, two random variables X and Y are independent if the relevant PDF&s;s satisfy p(xly) = p(x), or equivalently p(x, y) = P(x)P(Y).