An introduction to Jeffreys priors - 2
Вставка
- Опубліковано 14 тра 2018
- 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.uk/Students-Gui...
For more information on all things Bayesian, have a look at: ben-lambert.com/bayesian/. The playlist for the lecture course is here: • A Student's Guide to B...
when you write the log likelihood (1:00), the expression inside the brackets is posterior but not the likelihood.
this other video of dr.lambert’s might clear things up a bit. ua-cam.com/video/IhoEwC9R8pA/v-deo.htmlsi=TSzEfWCRUN4JEIrD
I am not used to leaving comments under youtube videos but big thanks to you Ben. Definitely not the first nor the last video of yours that I will be watching
Thank you for sharing this insight. Straight and simple.
isn't it supposed to be l x given theta?
excellent
why can you cancel the integrals in this case?
This has been puzzling me as I see this happening in another video in this series! Can somebody please clarify?
It's really sloppy. What he is actually doing is applying a Jacobian.
still have no idea. Ben can you decipher this to a non-PhD statistician in a practical sense. Ii.e. some data examples? It is impenetrable.
TBF to Ben, it's a set of videos about a notorious theoretical issue that led to Bayesian inference being deemed "unusable" for many years. As such, the videos are dealing with a broad problem of "what if two people choose to define the same question in slightly different ways". Numerical examples would hide the nature of the solution to the bigger problem