Відео

thanks

Beautiful!!!👌

Wonderful explanation of a difficult topic!

Really valuable content, thanks a lot !

I would say that this is the best video i have ever seen for Stochastic Process. Hats off!

What a video, really useful! Thank you very much.

Thank you so much, I love the examples you used to explain the topic

Super helpful!!! thank you!

Thank you so much. Your explanation is very clear and easy to understand.

I am learning first year linear algebra and felt I have gotten a good gist of how humans have managed to measure chaos. Good job and a testament to your explaining skills!

Wonderful! The instructor knows how to teach indeed! Thanks.

Thank you so much ❤❤❤❤❤

Thank you very much for the clear explanation.

Thank you very much Great content. I was having a hard time understanding sss processes. Really helped me understand.

Great examples.

Thank you so much!

Nice! Thank you.

Thank you for your interpretation of R Vs. This is the clearest explanation of this concept on UA-cam so far I found.

Very nice Video!

Great video! Do you know a good book of exercises/problems about stochastic processes with solutions? I would like to practice before my exam Thanks in advance

Please make more videos on bayesian statistics. Your teaching is very clear!

Is golf a stochastic process?

Simple and understandable explanation! Makes it much easier to learn.

Great.

Very helpful... thank u..

thank you, sir

Incredible video

He wrote Estimation error as (Q^ - Q) first then in MSE he wrote (Q-Q^)..????

Incredibly helpful! Without dragging through ridiculous notation, thank you very much for uploading!

puh puh primes

Everyone will see in these charts something for himself. I see GARCH process )))

what mean sample realization length 30 ? is that similar with figure 3 sample?

Great video! Explained it easily and simply

helpful explanations. examples always better to give for each case.

Thankyou very much for the explanation. One of the best and useful explanations given for discrete kalman filter.

nicely delivered

Excellent explanation, thanks !

That is a good way to explain that as we have LTI systems for deterministic signals, we narrow down Random process similarly to make our life easier into Time-invariant random processes.

thank you

Here is a teacher that probably didn't realise in his life that he is a genius in teaching

There are proffesors and there are those who make everybody to understand what was professor talking obout :-)

Thank you Sir; very interesting remark in 13:38

Thanks sir

Thanks sir

Please were can i find your lectures notes ?

It took my teach 2 full hours to explain Kalman filter and we undertook nothing. Thank's man, you took me out of the hell

thanks

1:05 why 'where' is written as 'whore'

you rock Carles, this is one of the most intuitive and easy ways to explain Kalman filters I saw so far. Thanks a lot fo such nice material.

for example one , i think E[W1]= W1 instead of 0?