An introduction to Jeffreys priors - 3

These series of videos explain what is meant by Jeffreys priors as well as how they satisfy a particular notion of ‘uninformativeness’. This concept is explained through a simple Bernoulli example.
This video is part of a lecture course which closely follows the material covered in the book, "A Student's Guide to Bayesian Statistics", published by Sage, which is available to order on Amazon here: www.amazon.co....
For more information on all things Bayesian, have a look at: ben-lambert.co.... The playlist for the lecture course is here: • A Student's Guide to B...

[@spyridon6044]

Greatly explained!! :)

[@rangjungyeshe]

Gave me a new understanding of what Jeffreys rule is really intended to achieve. Most textbooks give the distinct impression it's all about ensuring that the priors are equivalent - when (of course), the real goal is to make sure that the inferences (as given by the posteriors) are invariant to (monotonic) changes of variable.

[@SantiagoZuluaga]

Thank you Ben, amazing videos!