18. Bayesian Statistics (cont.)

MIT 18.650 Statistics for Applications, Fall 2016
View the complete course: ocw.mit.edu/18-...
Instructor: Philippe Rigollet
In this lecture, Prof. Rigollet talked about Bayesian confidence regions and Bayesian estimation.
License: Creative Commons BY-NC-SA
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[@lazywarrior]

At 11:00, he is doing the univariate case, so the det part can be omitted. For the multivariate case, you need the additional fact that det(AB) = det(A)*det(B), and the result follows similarly.

[@chaitanyakandwal7827]

Thank you so much Prof. Rigollet !

[@mathom21]

This is what I was looking for. Mamy thanks MIT.

[@not_amanullah]

this is helpful ♥️🤍

[@not_amanullah]

thanks ♥️🤍

[@brainstormingsharing1309]

Absolutely well done and definitely keep it up!!! 👍👍👍👍👍

[@jasonqian746]

Really is an excellent lecture, helped a lot with my understanding of Bayesian Stats. In the Non-informative priors section, is the unbounded case actually where an improper prior needed?

[@xuchuan6401]

No, for example, u(0,1) is also a non-informative prior

[@콘충이]

Thanks!

[@henry_immanuel02]

❤️