For a drift diffusion process
is in differential form. The in integral form
is defined by a regular Reinmann integral and an Ito integral with respect to a Weiner process. Expanding their definitions is
Let and .
Now consider an infinitelly differentiable, square integrable function that is a function of the vector.
We can put in a dummy value equal to zero
And move the 2 endpoints into the summation
where the endpoints are and , and all the other indices are copied . Then make
And taking the limit it is still valid
We can make the sum a partition
Since will become arbitrarily small it will always be in the radius of convergence for a Taylor series. So we can expand the difference of the inside of the sum to a Taylor series.
Let
So
Here is the gradient and is the Hessian and so on. If we expand the gradients and Hessians to a sum
one can see that
and so
Get rid of the first term
For the first part let
Then
So
And inside the summation is
and doing the same thing with a lower bound we know the first part goes to zero
The second term
Looking at the second term
We can expand the dot product to
If we take the expected value then
Swap in the definition of Ito integrals
Factor out the limit and swap it with the expected value (I think this works but not sure)
then rearange
The are are independent, so their covariance is always zero if . So in the sume we only keep the diagonals
And the increments in are also independent by definition of a Weiner process, so the expected value goes to zero for different increments
And the variance of a Weiner processes increment is by definition the increment of time, so
And now its just the definition of a Reinmann integral
Third and Fourth terms
So with
inside the summation it looks like
If we use Cauchy Schwarz inequality where then
And we've seen that the first term goes to zero, and the second term is finite so
and we don't have to worry about it at all
Taylor series substituted
So substituting what we have, where only 1 of the terms isn't zero gives
Looking back at the Taylor series if we take the expected value
and the higher order terms other than these ones go to zero, pretty easy to prove similar to others but will skip it for now
The first part is just a Reinmann integral
Substitute
and adding the integrals just concatenates them so
The expected value of just the drift term is zero so
Then if we substitute again
and the integrals just concatenate again so the limit doesn't matter
Then let
so then
So we get the formula expression
And since its deterministic you can do
final part
We also know that
so taking the derivative of both sides
and substituting
Divergence theorem
Split the integral up
first part
focusing on the first part
We can write it like this
and remember that a scalar and a vector
And with the divercence theorem
And if its two of then dotted then
so
And if we assume the probability goes to zero at the edge (or in the limit to infinity) then the boundary condition dissapears
second part
TODO, just the same thing but twice
result
So we have
and since it works for arbitrary function f it must be that