
#1
Nov1809, 01:57 AM

P: 1,270

" The law of total expectation is: E(Y) = E[E(YX)].
It can be generalized to the vector case: E(Y) = E[E(YX1,X2)]. Further extension: (i) E(YX1) = E[E(YX1,X2)X1] (ii) E(YX1,X2) = E[E(YX1,X2,X3)X1,X2] " ==================== I understand the law of total expectation itself, but I don't understand the generalizations to the vector case and the extensions. 1) Is E(YX1,X2) a random variable? Is E(YX1,X2) a function of both X1 and X2? i.e. E(YX1,X2)=g(X1,X2) for some function g? 2) Are (i) and (ii) direct consequences of the law of total expectation? (are they related at all?) I don't see how (i) and (ii) can be derived as special cases from it...can somone please show me how? Any help is much appreciated! 



#2
Nov1809, 03:26 PM

P: 523





#3
Nov1909, 12:45 PM

P: 1,270

∞ ∫ y f(yx) dy ∞ for continuous random variables X and Y. (similarly for discrete). General definition: E(YA)=E(Y I_A)/E(I_A)=E(Y I_A)/P(A) where I_A is the indicator function of A. If it's too hard to show it in general, can you please show me how can we derive (i) and (ii) from the law of total expectation for the case of CONTINUOUS random variables? Thanks! 



#4
Nov1909, 03:37 PM

P: 523

law of total expectation (VECTOR case) 



#5
Nov2009, 08:26 PM

P: 1,270

First of all, is E(YX1,X2) a function of X1 and X2??
Is E(YX1) = E[E(YX1,X2)X1] a special case of the law of total expectation E(Y) = E[E(YX)]? 


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