Thank you Prof Parrondo for your wonderful lectures! I have watched your Part 1-3 and you have demystified the complex world of SDE! Can I ask how did you derive \sigma^2 \Delta t at 8:44 mins? I am confused. Is it because dW is \Delta x and in your earlier videos, you define \frac{\Deltax^2}{\Delta t} = \sigma^2?
May I ask? I might be wrong but Ito calculus seems to have a problem with oscillating terms. Assume that instead of dot(x) we had i dot(x). Then we would expect that the fluctuations and random noises would just change the frequencies in the problem. But due to the extra term, g^2 ->-g^2 we would have a d.amping. Why? What do I miss?
cool, thank you very much for the lecture Prof. Could you please post some videos about SPDE (Stochastic Partial Differential Equation)?
Bravo, I am your fan now. Please post the whole lectures.
Thanks!! You have all the lectures on my channel
really enjoyable lecture
Thank you Prof Parrondo for your wonderful lectures! I have watched your Part 1-3 and you have demystified the complex world of SDE! Can I ask how did you derive \sigma^2 \Delta t at 8:44 mins? I am confused. Is it because dW is \Delta x and in your earlier videos, you define \frac{\Deltax^2}{\Delta t} = \sigma^2?
Expand it and use the concept from part 1 that cross correlation is sigma square time minimum time you will get it.
May I ask? I might be wrong but Ito calculus seems to have a problem with oscillating terms. Assume that instead of dot(x) we had i dot(x). Then we would expect that the fluctuations and random noises would just change the frequencies in the problem. But due to the extra term, g^2 ->-g^2 we would have a d.amping. Why? What do I miss?
At 12:55 why g^2 dW is replace by sigma ^2 dt. How you have ignored g ^2 term
I think that was error in writing
Thanks for making concept easier. Can you share some references as well like books