Lecture for the course Statistical Physics (Master on Plasma Physics and Nuclear Fusion). Universidad Complutense de Madrid. Course webpage: seneca.fis.ucm....
I've searched a lot for good SDE tutorials. This was the best video series by far. Nothing, and I mean nothing, is taken for granted. Everything is rigorously explained and even if I didn't get something there was a concrete reference to go look at and then easily jump back. Enhorabuena profesor y muchisimas gracias!
Hello SIr i am completely new to all of this, what basic knowledge do i need to understand stochastic differential equations or on this video is this the basic knowledge i need to know. i hope what is said makes sense?
I think you can follow it if you know differential calculus, a bit of differential equations, and basic probability (gaussian variables, central limit theorem, average, independent random variables)
I'm not sure what I'm misunderstanding at 35:00, the expectation of w(t) is 0 b/c w(t) ~ N(0, σ*root(t)), shouldn't the expectation of w(t)^2 = Var(W) which equals σ*root(t)? rather than σ^2*t
I use the notation N(mu,sigma) to indicate a gaussian random variable with average mu and dispersion sigma (=> variance=sigma^2). I've just learned that the standard notation is N(mu,sigma^2). Sorry for the confusion! The Wiener process at time t is a gaussian variable with zero average and dispersion sigma*sqrt(t) => variance = sigma^2*t
This is a good one specially if you are interested in simulations (but ok for theory as well): www.amazon.es/Stochastic-Numerical-Methods-Introduction-Scientists/dp/3527411496
I've searched a lot for good SDE tutorials. This was the best video series by far. Nothing, and I mean nothing, is taken for granted. Everything is rigorously explained and even if I didn't get something there was a concrete reference to go look at and then easily jump back. Enhorabuena profesor y muchisimas gracias!
Very nicely explained, I very much liked the balance between intuition and formality.
Thank you very much juan.
Do you have any notes available for the public .
Thank you in advance.
This was very helpful, Juan
Muchísimas gracias Profesor.
Hello SIr i am completely new to all of this, what basic knowledge do i need to understand stochastic differential equations or on this video is this the basic knowledge i need to know. i hope what is said makes sense?
I think you can follow it if you know differential calculus, a bit of differential equations, and basic probability (gaussian variables, central limit theorem, average, independent random variables)
Gracias Juan
I'm not sure what I'm misunderstanding at 35:00, the expectation of w(t) is 0 b/c w(t) ~ N(0, σ*root(t)), shouldn't the expectation of w(t)^2 = Var(W) which equals σ*root(t)? rather than σ^2*t
I use the notation N(mu,sigma) to indicate a gaussian random variable with average mu and dispersion sigma (=> variance=sigma^2). I've just learned that the standard notation is N(mu,sigma^2). Sorry for the confusion! The Wiener process at time t is a gaussian variable with zero average and dispersion sigma*sqrt(t) => variance = sigma^2*t
@@juanmrparrondo1375 ah thank you so much Professor, this video helped me greatly
Loved it
great video thanks!
Sir Please tell me the basic books for stochastic differential equation and stochastic fractional differential equations please sir🙏
This is a good one specially if you are interested in simulations (but ok for theory as well): www.amazon.es/Stochastic-Numerical-Methods-Introduction-Scientists/dp/3527411496
👍 Thanks
thank you. please what is the programme that you use in these video (writing in table)
Hi. It is doceri: doceri.com/. It's a great app for the ipad. You can record the writing and play it in the class at the speed that you wish.
@@juanmrparrondo1375 muchos gracias
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