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).
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?