You should only use wide priors if you have no information about your data. It should usually be possible to come up with more informative prior distributions by reading other studies in your area or talking to experts.
Yes and no. With quality data, the prior is overwhelmed by the data, so the prior shouldn't matter too much as long as it's in the right ballpark. And translating abstract information from other studies and experts into a prior distribution is surprisingly challenging.
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I wish our econometrics professors were teaching like you. Outstanding!!
Just learned more in this brilliant lecture than the whole semester!! GRACIAS!!! ❤️🇲🇽❤️
oh Waaw! I paid around a 10 dollars to learn this in my university and this guy does it better than anyone is had seen!
One of the bast classes ever! Congrats.
Running bin_unif
Can you please clarify the fact the no multiple testing correction is needed for Bayesian approach - thank you
Can you please provide the link to download the dataset used in the video?
Unfortunately, the data is confidential. So while I can share the analysis, I'm not allowed to share the raw data. Sorry about that.
hello, how can i define a prior from a previous experiment?
wow!! could be the best lecture i ever heard! thank you!!
This was incredibly helpful, thanks for sharing this video!
Great lecture!
I know the beauty of Bayesian now!
Fantastic lecture. Thank you
You should only use wide priors if you have no information about your data. It should usually be possible to come up with more informative prior distributions by reading other studies in your area or talking to experts.
Yes and no. With quality data, the prior is overwhelmed by the data, so the prior shouldn't matter too much as long as it's in the right ballpark. And translating abstract information from other studies and experts into a prior distribution is surprisingly challenging.
Great lecture, thanks!
wow this resolves my confusion between the 2
Really helpful thank you very much!!
brilliant man. Period.
Wow