I think I'd like to give some clarification here after I plot the change of the marginal likelihood with the prior in python. The plots in the video are not very clear. The prior should be a function of a but not mu. The same for the marginal likelihood, it's also not a function of mu, as mu is integrated out. On the contrary, likelihood and posterior are functions of mu.
This was a really nice illustration. Thanks for all of your videos, they're very helpful!
Hi, Ben! Where can we find the notebook that you use in the video to play around?
its in my github repo
I don't really understand why Marginal Likelihood (P(X)) change with prior (mu), since it's already integrated over mu. Shouldn't it be a constant?
I think I'd like to give some clarification here after I plot the change of the marginal likelihood with the prior in python. The plots in the video are not very clear. The prior should be a function of a but not mu. The same for the marginal likelihood, it's also not a function of mu, as mu is integrated out. On the contrary, likelihood and posterior are functions of mu.