Please correct me if I am wrong. I am not sure we need the last line in the model block: theta ~ uniform(0,1) in principle. Though in the case of uniform theta, it does not matter here, but if theta is non-uniform distribution, we shall not have this line as log_sum_exp(lq) includes all terms.
Remove log(p(theta)) is correct but the reason is not that theta is not term being summed as mentioned in the video but because theta is uniform distribution.
Hi teacher. You do not believe how useful it’s been to me. Still waiting for a course on matlab, eviews or another software to undertake econometric analysis. Also interested in non parametric regression if it is not too much asking. If that is posible you would have literally saved my life. Thanks anyway.
God bless you! The explanation of marginalization in the Stan documentations was quite confusing and this video made it understandable for me. Thanks!
Hi, Ben, God bless you for your amazing video, and UA-cam for recommending this video to me.
Clear explanations with hands-on R examples!
thank you very informative
Hi, Ben, In 9:42 , since log( p(n) ) = 0.25, we also can remove it, right?
Of course this is only true if n has discrete uniform distribution.
Please correct me if I am wrong.
I am not sure we need the last line in the model block: theta ~ uniform(0,1) in principle. Though in the case of uniform theta, it does not matter here, but if theta is non-uniform distribution, we shall not have this line as log_sum_exp(lq) includes all terms.
I'm sorry to ask but may I know which application is getting used to create this beautiful video ?
Remove log(p(theta)) is correct but the reason is not that theta is not term being summed as mentioned in the video but because theta is uniform distribution.
Hi teacher. You do not believe how useful it’s been to me. Still waiting for a course on matlab, eviews or another software to undertake econometric analysis. Also interested in non parametric regression if it is not too much asking. If that is posible you would have literally saved my life. Thanks anyway.