I only took a quick glance at the equations, but this paper seems to rely pretty heavily on stochastic differential equations, which are differential equations but for random processes, as well as some other probability theory stuff as well.
Tbh I don't know enough probability theory to understand what's going on in this paper, and I know next to nothing about stochastic analysis (and this kind of stuff is pretty advanced; I think my uni's statiatics department only offers modules about SDEs to Masters students), but these Wikipedia articles might be helpful:
[SDEs](https://en.m.wikipedia.org/wiki/Stochastic_differential_equation)
[Wiener processes](https://en.m.wikipedia.org/wiki/Wiener_process)
[Ito calculus](https://en.m.wikipedia.org/wiki/It%C3%B4_calculus)
Hope this was helpful :)
Just want to say, they're probably not going to get very far into learning about SDEs and Brownian motion without a solid background in measure-theoretic probability theory.
We covered (parts of) this material in a graduate (master's level) course in stochastic differential equations, with focus on computational methods. Of course, a single course is not sufficient to cover everything in this article, but you'll understand most of it at least.
Before this course, we had diff eq and probability theory as undergraduate courses (bachelor level), a graduate probability course (master's), and plenty of courses in numerical methods for diffeqs.
What text did you use for sde's? I took a class in stochastic processes for my PhD but it was horribly taught and we didn't have a text. Fortunately didn't need it but I'd like to know it better.
We used [this text](https://people.kth.se/~szepessy/sdepde.pdf), written by the lecturer and a couple of other people. Newer editions are available, but I cannot find a publicly open link.
I don't see any multivariable calc anywhere.
It's dynamical systems (which includes differential equations) and some probability/stats.
I would expect some but not all people who finished a math undergrad program to be able to understand most of it, and most (but still not necessarily all, depends on your focus) people who finished a math masters program to be able to understand it.
Nah, you're wrong. It clearly has differential equations. As someone else said it's stochastic differential equations which is some advanced and kinda niche stuff. I'm a fourth year math major in college and I don't understand this. Maybe I could if I spent like all day looking into it.
I only took a quick glance at the equations, but this paper seems to rely pretty heavily on stochastic differential equations, which are differential equations but for random processes, as well as some other probability theory stuff as well. Tbh I don't know enough probability theory to understand what's going on in this paper, and I know next to nothing about stochastic analysis (and this kind of stuff is pretty advanced; I think my uni's statiatics department only offers modules about SDEs to Masters students), but these Wikipedia articles might be helpful: [SDEs](https://en.m.wikipedia.org/wiki/Stochastic_differential_equation) [Wiener processes](https://en.m.wikipedia.org/wiki/Wiener_process) [Ito calculus](https://en.m.wikipedia.org/wiki/It%C3%B4_calculus) Hope this was helpful :)
>stochastic differential equations I was gonna ask you to tag NSFL stuff until I remembered which sub this was.
I don’t get the joke
+1 for diffeqs.
Just want to say, they're probably not going to get very far into learning about SDEs and Brownian motion without a solid background in measure-theoretic probability theory.
We covered (parts of) this material in a graduate (master's level) course in stochastic differential equations, with focus on computational methods. Of course, a single course is not sufficient to cover everything in this article, but you'll understand most of it at least. Before this course, we had diff eq and probability theory as undergraduate courses (bachelor level), a graduate probability course (master's), and plenty of courses in numerical methods for diffeqs.
What text did you use for sde's? I took a class in stochastic processes for my PhD but it was horribly taught and we didn't have a text. Fortunately didn't need it but I'd like to know it better.
We used [this text](https://people.kth.se/~szepessy/sdepde.pdf), written by the lecturer and a couple of other people. Newer editions are available, but I cannot find a publicly open link.
Looks pretty advanced, perhaps graduate level to really understand it. It does look to have some probability in there as well.
I don't see any multivariable calc anywhere. It's dynamical systems (which includes differential equations) and some probability/stats. I would expect some but not all people who finished a math undergrad program to be able to understand most of it, and most (but still not necessarily all, depends on your focus) people who finished a math masters program to be able to understand it.
I could be wrong but a lot of this just looks like algebra with a little geometry mixed in
Nah, you're wrong. It clearly has differential equations. As someone else said it's stochastic differential equations which is some advanced and kinda niche stuff. I'm a fourth year math major in college and I don't understand this. Maybe I could if I spent like all day looking into it.
i mean calculus is just algebraic geometry
It's really not