Hello again, your RStan videos which I've watched repeatedly are a treasure trove. They've really helped me to understand the whole process of doing Bayesian inference in Stan. PS: Please where can I find the music in the intro (you described it as 'Soul Hip Hop Beat' by UrbanGodzilla but can't find it anywhere on the web)??? I got to have this its too funky! Any help with this will be great. Thanks again.
Thank you very much. A very helpful video. So far a task that could far easier be done using rstanarm but I hope to encounter the higher flexibility of programming directly in Stan. Should you still be planning on producing further videos (which I hope) and should you be accepting requests: As the Bayesian alternative to LASSO apparently Horseshoe priors are great. However, I still have to find a single TH-cam video to explain those and how to use them in an accessible manner. Cheers.
Hi Harmonica! Yes i'm still active, but been caught up with other projects unfortunately. Rstanarm is a great wrapper package, but as you say it's way less flexible. Sure thing - I can do a video on horseshoe priors. Will add it to the list :)
@@learnstanwithric I have been searching for horseshoe as well. I want to create a complex model using the horseshoe but can't figure how to model it! I used brm to get the stan code for horseshoe then try to modify it. But I still can't get it T_T
I'm not sure I understand what you are doing in the step around 17:25 where you used vectorized versions of the probability statements that were previously for loops. You go through the hassle of writing for loops for the code and then seemingly delete them and just say "you can vectorize these instead." What then is the purpose of using the for loop if we can do this instead? It seems far more straightforward to just use the vectorized version than the for loop step before it, thus making the for-loop step somewhat unnecessary and confusing in this video.
![The Wall Song ร้องข้ามกำแพง | EP.215 | พาเวล นเรศ - พูห์ กฤติน | 17 ต.ค. 67 [1/5]](http://i.ytimg.com/vi/IUzrjuf60P4/mqdefault.jpg)
Incredibly useful mate. Many thanks!
I have found your videos super helpful! Thank you!
Very helpful - explanation on assumptions and setting up of priors is excellent. I have subbed 👍
Hello again, your RStan videos which I've watched repeatedly are a treasure trove. They've really helped me to understand the whole process of doing Bayesian inference in Stan. PS: Please where can I find the music in the intro (you described it as 'Soul Hip Hop Beat' by UrbanGodzilla but can't find it anywhere on the web)??? I got to have this its too funky! Any help with this will be great. Thanks again.
Thank you very much. A very helpful video. So far a task that could far easier be done using rstanarm but I hope to encounter the higher flexibility of programming directly in Stan. Should you still be planning on producing further videos (which I hope) and should you be accepting requests: As the Bayesian alternative to LASSO apparently Horseshoe priors are great. However, I still have to find a single TH-cam video to explain those and how to use them in an accessible manner. Cheers.
Hi Harmonica! Yes i'm still active, but been caught up with other projects unfortunately. Rstanarm is a great wrapper package, but as you say it's way less flexible. Sure thing - I can do a video on horseshoe priors. Will add it to the list :)
@@learnstanwithric I have been searching for horseshoe as well.
I want to create a complex model using the horseshoe but can't figure how to model it!
I used brm to get the stan code for horseshoe then try to modify it. But I still can't get it T_T
I'm not sure I understand what you are doing in the step around 17:25 where you used vectorized versions of the probability statements that were previously for loops. You go through the hassle of writing for loops for the code and then seemingly delete them and just say "you can vectorize these instead." What then is the purpose of using the for loop if we can do this instead? It seems far more straightforward to just use the vectorized version than the for loop step before it, thus making the for-loop step somewhat unnecessary and confusing in this video.