Quote:
Originally Posted by PairTheBoard
Could you show the form of a simple 2,3, or n dimensional SDE
Here is a simple 2-dimensional SDE:
dX = m(X) dt + s(X) dB.
The solution, X, will be a process taking values in R^2; B is a 2-dimensional Brownian motion. The function m maps R^2 to R^2, and the function s maps a point in R^2 to a 2 x 2 matrix. The above equation is shorthand for the pair of equations
dX_1 = m_1(X) dt + s_{11}(X) dB_1 + s_{12}(X) dB_2,
dX_2 = m_2(X) dt + s_{21}(X) dB_1 + s_{22}(X) dB_2.
(Think of X, m(X), and dB as 2 x 1 column vectors; dt is a scalar; and s(X) is a 2 x 2 matrix.) In turn, these are shorthand for the integral equations
X_i(t) = X_i(0) + \int_0^t m_i(X(s)) ds
+ \int_0^t s_{i1}(X(s)) dB_1(s)
+ \int_0^t s_{i2}(X(s)) dB_2(s),
where the last two integrals are Ito integrals.
Quote:
Originally Posted by PairTheBoard
and then provide a simple intuitive explanation of what the terms are describing in the sense of local probability flows?
I am not sure what you mean by the above term in bold. Does this refer to the way the probability density function of the solution changes with time? Under suitable assumptions on m and s, the solution X(t) has a density function f(x,t) that satisfies the
Kolmogorov forward equation (also called the Fokker–Planck equation).
Personally, I do not find this to be a very intuitive way of thinking about the dynamics described by the SDE. I prefer to think about the paths of X, rather than the densities f(x,t). Informally, I would write
dX/dt = m(X) + s(X) W(t),
where W(t) = dB/dt. The "white noise" term W(t) exists only as a generalized function, but you can think of it informally as a stationary, R^2-valued process that gives an infinitesimal random push at time t to X. The push has no preferred direction and is independent of the pushes given prior to time t. The usual example is a pollen grain on the surface of still water being pushed by collisions with the surrounding water molecules.
In this case, the term m(X) gives a deterministic drift, and the term s(X) serves to modify the magnitude of the noise term and, possibly, give it a preferred direction. (Here, "direction" does not mean a vector, but a line through the origin. The modified noise term will still have mean 0.) In this sense, the term s(X) is changing the shape of the density of the noise term. But this is all completely informal. Rigorously, the noise term is not even a random variable (in the usual sense), so there is no density whose shape can change.
Quote:
Originally Posted by PairTheBoard
If so, could you give a real world example where you might think you know how the local probability should be flowing and thereby just write down the corresponding SDE to model the situation?
There should be some nice examples in Oksendal that illustrate what I described above. If not, you should be able to cook something up yourself. Maybe you could model a pollen grain on a gently drifting pool of water whose temperature varies spatially.