Conjugate Prior for Variance of Normal Distribution with known mean

This is a demonstration of how to show that an Inverse Gamma distribution is the conjugate prior for the variance of a normal distribution with known mean.
These short videos work through mathematical details used in the Multivariate Statistical Modelling module at UWE.

[@garikco]

This is super helpful. Much clearer than my graduate econometrics course

[@TuNguyen-ox5lt]

it really helps me a lot . I stucked when i got to find posterior distribution for mu with unknown mu and variance . I just figured out when i saw your video . Thanks

[@nshmr1]

This is great!

[@jinsillee21]

Thank you so much!

[@nikky2609]

Thanks this is great

[@josephsantarcangelo9310]

Thanks !

[@lemyul]

thank you

[@viperz301]

THANK YOU

[@thyenchen2765]

really useful

[@uhaankuku]

how can we show that the wishart distribution is a conjugate prior of the multi variate normal distribution

[@SaidulIslam-pv7dx]

F

[@rolfjohansen5376]

I am able to do the likelihood, but I am looking for the formula calculating the prior distribution for the gaussian mean parameter .....!!

[@deetoher]

Is this the one you are looking for?
It works on the basis of a known variance.
ua-cam.com/video/c-d05z0_5mw/v-deo.html

[@rolfjohansen5376]

@@deetoher your reply is chockingly fast, I will take a look at and try to grasp how the prior was constructed ... thanks

[@rolfjohansen5376]

@@deetoher saw your vid, from my beginning perspective I understand now that the prior is a distribution without data , but shall combined with a likelihood to be able to manipulate data. I was a little confused at first as priors are something you choose and they got types and properties (example: conjugate priors are good for updating information, proper/improper priors). I did not get a clear picture why you did choose exactly "that" prior , or why you preferred it in this setting based on certain needs you might have. Thanks! (now a subscriber)