- #1

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P(x < f(y) | x > -f(y))

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- Thread starter rabbed
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In summary, the conversation discusses using the CDF method for a random variable as a function of X. The goal is to convert P() expressions to X_CDF() expressions. The speaker also mentions needing to reformulate expressions and asks about the case of having P(A + B < y) with knowledge of A_PDF(a) and B_PDF(b).

- #1

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P(x < f(y) | x > -f(y))

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- #2

Science Advisor

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Your expression can be given as [itex]\frac{P(-f(y)<x<f(y))}{P(-f(y)<x<\infty)}[/itex].

- #3

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P(x > -f(y) AND x < f(y)) =

P(x > -f(y)) * P(x < f(y) | x > -f(y))

The aim is to convert the P()'s to X_CDF()'s.

Your answer led me back a step, which made me think that maybe P(x > -f(y) AND x < f(y)) might be expressed like (1-X_CDF(-f(y))) - X_CDF(f(y))

It seems to be correct for my case, so thank you :)

- #4

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In the CDF method, I understand that I need to reformulate expressions to get something like P(X < y) which equals X_CDF(y) or P(X > y) which equals (1-X_CDF(y)), since I know the expression of X_PDF(x) = X_CDF'(x).

What if I have P(A + B < y), knowing A_PDF(a) and B_PDF(b)?

Would that require that I know AplusB_PDF(a,b) and some transformation from y to a and y to b?

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