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When we don't know what's the mean of a normal distribution we try to estimate it and after a time we get lucky and have the true mean (in a magical way). What does it mean the expected value of the estimator equals to the true mean ?

How can I have an intuition for the expected value of an estimator? (I know the equation and how to calculate it but I don't have the ability to imagine it as a graph or something)

kjetil b halvorsen
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Abderrahmen Hamdi
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    Imagine you create many datasets from the same population (i.e. the same distribution) and for each of those datasets, you compute the estimator, which gives you a dataset of estimated values. And then consider the expected value of that dataset. – frank Oct 13 '22 at 05:20
  • very nice explanation. – mlofton Oct 13 '22 at 05:53
  • Thank you @frank for the simple answer, can you also explain to me the expected value of y conditioned over theta (regression function), in bishop's book he mentioned that in the sequential estimation of theta we have to find a theta* which make the regression function equals to 0 which is the same as making the expected value of (y|theta) equals to 0. – Abderrahmen Hamdi Oct 13 '22 at 09:24
  • I suggest that you either change your current question or ask a new one, including references to your citations. – frank Oct 13 '22 at 09:56

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