The illusion of uninformative priors

This video explains why priors always - in some frame of reference - convey information and, hence, can not, as might be expected, be devoid of information.
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...

[@orktv4673]

This is a compelling argument, and I agree: there really is no way of choosing the "right" prior distribution based on no data whatsoever, and even if you could that prior would not be consistent.

[@CBJrocks]

How did we just remove the integrals at 7:40?

[@gordongoodwin6279]

Because they aren’t necessary, the point is that the area under the curve and the pdf itself are equivalent, he just put them there at first for clarity

[@hectorlavaux343]

The title is quite misleading, since you talk about the principle of indifference. You can define 'uninformative' otherwise, and then the concept becomes meaningful.

[@rangjungyeshe]

Nice example - not seen it before.

[@imranh1225]

Also at 8:11, why is P(theta) just 1?

[@OndrejHavlicek]

Because it is a uniform distribution and has value 1 everywhere?

[@gordongoodwin6279]

Yes, p(theta) is proportional to 1 because it’s uniform