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A part of the lecture notes for cs723 - probability and stochastic processes course. It covers the transformation of random variables, finding the derived pdf, and calculating moments using examples. Formulas and steps to compute moments directly from the joint pdf of x and y, and also using the marginal pdfs of u and v.
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Y
X
Y
X
uv
Xy
Z
Xy
X(
X 2 Y
Y 2 Y
2 Y
X
Y
Y
dx
(x) f
g(x)
dy
(y) f )
(y
dx
(x) f
g(x)
dy
(y) f y
X
X 2 Y
Y 2 Y
2 Y
X
Y
Y
dx
(x) f
g(x)
dy
(y) f )
(y
dx
(x) f
g(x)
dy
(y) f y
Some examples of simple transformations
2 X
2
2 Y
X
Y
2 X
2
2 Y
X
Y
2 X
2 Y
X
Y
XY
XY XY
E[g(x,y)] using joint PDF f
XY
(x,y)
XY
dy
dx
y)
(x,
f ) y , x ( g
y)
(x, g
E
dy
dx
y)
(x,
f ) y x ( Y X E
XY XY
E[g(x,y)] using joint PDF f
XY
(x,y)
y)
(x, g
E
dy
dx
y)
(x,
f ) y , x ( g
y)
(x, g
E
2 2 U
(^2) U
XY
U
XY
dy
dx y)
(x,
f ) y , x ( h ) y , x ( g
)
V )(
U(
E
XY
V
U
V
U
UV
Similarly, Examples of linear transformations functions
g(. ,. ) and h(. ,. )
To be continued …