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noninformative prioir (normal data) from http://www.stat.columbia.edu/~fwood/Teaching/w4315/Fall2009/nuisance_parameters.pdf

Can someone help me through the derivation? That is, how is summation of (yi-mu)^2 equal to the equation that follows? where did n(y(bar)-mu)^2 come from?

Thanks.

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    $\begingroup$ You provided half information. Can you provide more context of the data model? What is your likelihood? $\endgroup$ – Jon Aug 18 '17 at 3:53
  • $\begingroup$ I'm looking at the derivation of the posterior distribution when data is normal and prior in non-informative (1/sigma^2). Likelihood is for normal iid. $\endgroup$ – Erwin Aug 18 '17 at 5:26
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Take ($y_i$-$\mu$)$^2$ and add and subtract y bar inside the square and expand.

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