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Hi all..

I read one article couple days ago, yet, there is some equations that I could not understand.

let assume that y = u + v

where u is normally distributed with mean = 0 and variance = s -> u ~ N (0, s)

and v is normally distributed with mean = 0 and variance = t -> v ~ N (0, t)

thus, the author wrote that :

E (v_{i}|y_{i})= (t *y_{i})/(s+t)

I tried to find how he derived this conditional expectation.

E (v_{i}|y_{i})= Integral of x*Pr(v_{i}|y_{i}) dx

Then calculate Pr(v_{i}|y_{i}) using bayes rules.

However, it seems that I couldnot get the same answer as mentioned by the author in that article E (v_{i}|y_{i})= (t *y_{i})/(s+t)

Could some one please help me on this matter or show me the way to get the same result as the the author

Thank you..:)

ps : 1. v_{i} is v subscript i.

2. I tried to write using the math simbols (using LaTeX ref) but the results did not look good. That's why I used current style in this question. (i am very sorry for this)

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# Help me in conditio expectation

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