32 - Normal prior conjugate to normal likelihood - proof 2

This video provides a proof of the idea that a normal prior with a normal likelihood results in a normal posterior density.
If you are interested in seeing more of the material, arranged into a playlist, please visit: • Bayesian statistics: a... For more information on econometrics and Bayesian statistics, see: ben-lambert.com/

[@T4l0nITA]

This is the best explanation I was able to find, couldn't figure it out from the one on DeGroot textbook

[@benjaminchou1431]

excellent derivation for easy understanding!!!!

[@miaohanwang4694]

really really really helpful! thank you so much!!!

[@krazymugetsu89]

thanks for this video!!

[@jacobm7026]

I realize he stated that it was arbitrary to add those additional parts to complete the square, but I'm still not sure why that's allowed?

[@ianfukushima1316]

He is not using equality sign, he is using proportional sign.
So, for example, A = exp(B) is proportional to exp(B + C) = exp(B)exp(C).
In the demonstration, it is important that the added term doesn't depend on theta.

[@lemyul]

that looks like 12

[@lemyul]

i hate tricks, why cant we all just be honest