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EXAMPLE S5-3 Let X be a continuous random variable with probability distribution fX 1x2 x , 8 0 x 4

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Find the probability distribution of Y h(X ) 2X 4. Note that y h(x) 2x 4 is an increasing function of x. The inverse solution is x u( y) (y 4) 2, and from this we nd the Jacobian to be J u 1 y2 dx dy 1 2. Therefore, from S5-3 the probability distribution of Y is fY 1 y2 1y 42 2 1 a b 2 y 32 4 , 4 y 12

We now consider the case where X1 and X2 are continuous random variables and we wish to nd the joint probability distribution of Y1 h1(X1, X2) and Y2 h2(X1, X2) where the transformation is one to one. The application of this will typically be in nding the probability distribution of Y1 h1(X1, X2), analogous to the discrete case discussed above. We will need the following result.

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Suppose that X1 and X2 are continuous random variables with joint probability distribution fX1 X2 1x1, x2 2, and let Y1 h1(X1, X2) and Y2 h2(X1, X2) de ne a one-to-one transformation between the points (x1, x2) and (y1, y2). Let the equations y1 h1(x1, x2) and y2 h2(x1, x2) be uniquely solved for x1 and x2 in terms of y1 and y2 as x1 u1(y1, y2) and x2 u2(y1, y2). Then the joint probability of Y1 and Y2 is fY1Y2 1 y1, y2 2 fX1X2 3u1 1 y1, y2 2, u2 1 y1, y2 2 4 0 J 0 (S5-4)

This result can be used to nd fY1Y2 1 y1, y2 2, the joint probability distribution of Y1 and Y2. Then the probability distribution of Y1 is fY1 1 y1 2 fY1Y2 1 y1, y2 2 dy2

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That is, fY1 ( y1) is the marginal probability distribution of Y1. EXAMPLE S5-4 Suppose that X1 and X2 are independent exponential random variables with fX1 1x1 2 and fX2 1x2 2 2e 2x2. Find the probability distribution of Y X1 X2 . 2e

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