Відео

really nicely done. thanks

Beautiful voice and manner of speech

This is Gold! The best video explaining hierarchical bayesian models, it addresses many of the question I had, none of the other videos out there get to this level of details. it makes me feel more confident about using these models

Interesting tutorial

Great present. As far as i know, both uplift tree and T-learner are part of Potential Outcome Framework Model that predict CATE/ATE to know about the casual relationship.

Excellent explanation...Thank you!

Very nice, thanks a lot for the talk. Very well explained and easy to follow!

Very good explanations . Seen a lot of videos but your presentation is very understandable . Thanks

great one, currently looking for overlap between same treatment used for different diseases, this one looks very helpful to approach for synthesize

50:20 Make sure you know when to use variational inference instead of MCMC. (usually when working with large datasets)

What a great application of data science!

hands down the best explanation for hierarchical modelling on youtube, especially with the graph visualising the effect of pooling strength on the parameter values

One of the few well explained examples

Thanks a lot for this awesome presentation? Do you recommend particular packages and implementations on the topic?

Probably the clearest explanation of hierarchical models I’ve ever seen. Great video!

Amazing work. Thanks

Critical race theory is also quite racist. Just replace black with white in their writings, and you will see.

This is incredible. Thank you.

Amazing lecture!

The link to the blog referenced at the end is no longer accessible through it. Can I ask where I can find this blog?

Tunrayo taught me fractions - she really knows her stuff.

Great video, thanks !

Under Partial Pooling, why does sigma_a represent the degree of pooling?

Perhaps the best explanation. Thanks a hell lot

Wow, this has helped explained what hot deck is. Thank you!

❣️ 𝐩яⓞ𝓂𝓞Ş𝐦

Thanks for the informative video. In the end model in which uncertainty for different counties is very similar, isn´t the model understating this uncertainty for the cases with just 1 or 2 datapoints? Could you elaborate? Thanks!

Omar, it is not clear to me why sigma controls the amount of pooling. Could you point me into some sources to learn more about this? I enjoyed your presentation. Thanks.

this is really well explained.

Thanks for clear explanation. Very helpful.

Thanks Omar. Clear and helpful.

Bravissima.lezione eccellente

This is a great presentation. Learned a lot from it. Thanks for making it public.

Thank you for this well-paced video with its explanations. I feel much more confident in my understanding of Bayesian hierarchical modelling.

this company is corrupt and evil folks, expect another COMPAS scandal with their home office partnership, caveat emptor

This was great. Thank you Shailee

Had a hard time getting pystan to work on Windows. Anyy recommendation?

Very nice talk, Thanks

Good. There were reports of errors in facial recognition systems in use by Law enforcement agencies in the USA.

Great presentation and super interesting content!

This is brilliant use of predictive AI for customer segmentation, and perfectly presented. Well done sir!

great idea!

Wow, thats really brilliant. And a fantastically lucid presentation!!

That's a great project! Thank you for sharing.

very interesting!

What does the error audit stream look like, and can it not also perhaps suggest much more productive use than identifying and blocking (definitely not the film school definition of blocking, I take it.) Also, feel free to presume I'd like a TL:DR text description rather than to cite who's explaining, if that's not anathemical to fine 600.000,00 euro bloom filter cobblin'.