I recommend watching the regression sections multiple times; tons of points to learn and to look in the references. Extraordinary tutorials, thanks Steve.
9:18 I think b = a*x would be more accurate than b = anoisy*x. After all, it is the true factor a that has an influence on b, not the noisy measurement we took of a. I think this is the reason why the plot looked so much better than expected, also at 7:58, the noise was added to a before calculating b from a, so the noise didn't influence the relationship between a and b.
How can one calculate the uncertainty with svd? Say that we have uncertainty in both a and b, based on the uncertainty the slope and intercept of y should have uncertainty as well, can you please elaborate how to calculate the uncertainty in the slope and intercept. Thanks in advance.
the first method demon [time 6:45 - 7:19] is problematic explained as following: [U,S,V]=svd(a,'econ') input a doesn't change slope, so [U,S,V] won't change characteristics, xtilde=V*inv(S)*U'*b won't change its distribution from b's characteristics
When using the SVD command, you input your array a as a 1d array. This doesn't work in python. Does anyone know how to do this in python with a 1d array? Thank you
![Linear Regression 2 [Matlab]](http://i.ytimg.com/vi/_HTjxl_sdUA/mqdefault.jpg)
![Linear Regression 2 [Matlab]](/img/tr.png)
![SVD: Image Compression [Matlab]](http://i.ytimg.com/vi/QQ8vxj-9OfQ/mqdefault.jpg)
![[Live สด] การออกรางวัลสลากกินแบ่งรัฐบาล งวดวันที่ 16 พฤศจิกายน 2567](http://i.ytimg.com/vi/wPd7VFfWHIA/mqdefault.jpg)
![Unitary Transformations and the SVD [Matlab]](/img/n.gif)
I recommend watching the regression sections multiple times; tons of points to learn and to look in the references. Extraordinary tutorials, thanks Steve.
i replay the video after reading your comment I need to watch it again after testing on the real data
Just got your book! can't wait to dig in
Awesome! Thank you!
9:18 I think b = a*x would be more accurate than b = anoisy*x.
After all, it is the true factor a that has an influence on b, not the noisy measurement we took of a.
I think this is the reason why the plot looked so much better than expected, also at 7:58, the noise was added to a before calculating b from a, so the noise didn't influence the relationship between a and b.
wow, good job! I take back what i said about you lacking examples. this is top stuff
Thank you for the excellent explaination, looking Forward for more Video
How can one calculate the uncertainty with svd?
Say that we have uncertainty in both a and b, based on the uncertainty the slope and intercept of y should have uncertainty as well, can you please elaborate how to calculate the uncertainty in the slope and intercept.
Thanks in advance.
Really useful, thanks!
Thank you ❤
just out of curious. Is that a 13'' macbook or 16'' macbook?
Great video!
the first method demon [time 6:45 - 7:19] is problematic explained as following: [U,S,V]=svd(a,'econ') input a doesn't change slope, so [U,S,V] won't change characteristics, xtilde=V*inv(S)*U'*b won't change its distribution from b's characteristics
When using the SVD command, you input your array a as a 1d array. This doesn't work in python. Does anyone know how to do this in python with a 1d array?
Thank you
I understood what the problem was when watching the corresponding video in Python.
9:18 More like "the danger of mutable variables" :) Thanks for sharing, keep it up
x=a\b
First!
After we added some noise to a, should we run SVD with the noisy a rather than a in the 11th commend of the MatLab?