Fokker Plank Equation

For a drift diffusion process

dz=A(t,z)dt+𝐁(t,z)dWt

is in differential form. The in integral form

z(tb)z(ta)=tatbA(t,z(t))dt+tatb𝐁(t,z(t))dWt

is defined by a regular Reinmann integral and an Ito integral with respect to a Weiner process. Expanding their definitions is

=limΔtmax0(ti,ti+1)π(ta,tb)A(ti,z(ti))(ti+1ti))
+limΔtmax0(ti,ti+1)π(ta,tb)𝐁(ti,z(ti))(W(ti+1)W(ti)))

Let Δit=ti+1ti and ΔiW=W(ti+1)W(ti).

=limΔtmax0Δitπ(ta,tb)A(ti,z(ti))Δit+limΔtmax0Δitπ(ta,tb)𝐁(ti,z(ti))ΔiW

Now consider an infinitelly differentiable, square integrable function f(t,z(t)) that is a function of the vector.

f(t+k,z(t+k))f(t,z(t))

We can put in a dummy value equal to zero

=f(t+k,z(t+k))+j=1N(f(tj,z(tj))+f(tj,z(tj)))f(ta,z(ta))

And move the 2 endpoints into the summation

=i=0N(f(ti+1,z(ti+1))f(ti,z(ti)))

where the endpoints are t0=t and tN+1=t+k, and all the other indices are copied ti=tj. Then make

=i=0N(f(ti+1+Δi+1t,z(ti+1+Δi+1t))f(ti+Δit,z(ti+Δit)))

And taking the limit it is still valid

=limNi=0N(f(ti+1+Δi+1t,z(ti+1+Δi+1t))f(ti+Δit,z(ti+Δit)))

We can make the sum a partition

=limΔtmax0Δitπ(t,t+k)(f(ti+1+Δi+1t,z(ti+1+Δi+1t))f(ti+Δit,z(ti+Δit)))

Since Δt 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.

=limΔtmax0i(ftΔt+fz(z(ti+1)z(ti))+122f2tΔt2+)

Let

Δiz=z(ti+1)z(ti)

So

=limΔtmax0i(ftΔt+fzΔiz+122f2tΔt2+122f2zΔizΔiz+2ftzΔtΔiz+)

Here fz is the gradient and 2f2z is the Hessian and so on. If we expand the gradients and Hessians to a sum

=limΔtmax0i(ftΔt+kUfzkΔizk+122f2tΔt2+12kUlU2fzkzlΔizkΔizl+kU2ftzkΔtΔizk+)

one can see that

Δizk=titi+1A(t,z(t))kdt+titi+1𝐁(t,z(t))kdWt

and so

ΔizkΔizl=(titi+1Ak(t,z(t))dt)(titi+1Al(t,z(t))dt)
+(titi+1𝐁k(t,z(t))dWt)(titi+1𝐁l(t,z(t))dWt)
+(titi+1Ak(t,z(t))dt)(titi+1𝐁l(t,z(t))dWt)
+(titi+1Al(t,z(t))dt)(titi+1𝐁k(t,z(t))dWt)

Get rid of the first term

For the first part let

Mk=supt[ti,ti+1]Ak(t,z(t))
Ml=supt[ti,ti+1]Al(t,z(t))

Then

titi+1Ak(t,z(t))dtMkΔit

So

(titi+1Ak(t,z(t))dt)(titi+1Al(t,z(t))dt)MkMlΔit2

And inside the summation is

limΔtmax0ifzkzl(titi+1Ak(t,z(t))dt)(titi+1Al(t,z(t))dt)=limΔtmax0ifzkzlMkMlΔit2limΔtmax0ΔitmaxifzkzlMkMlΔit=0

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

(titi+1𝐁k(t,z(t))dWt)(titi+1𝐁l(t,z(t))dWt)

We can expand the dot product to

=(sUtiti+1𝐁k,s(t,z(t))dWt,s)(qUtiti+1𝐁l,q(t,z(t))dWt,q)
=sUqU(titi+1𝐁k,s(t,z(t))dWt,s)(titi+1𝐁l,q(t,z(t))dWt,q)

If we take the expected value then

=sUqU𝔼[(titi+1𝐁k,s(t,z(t))dWt,s)(titi+1𝐁l,q(t,z(t))dWt,q)]

Swap in the definition of Ito integrals

=sUqU𝔼[(limΔtmax0Δatπ(ti,ti+1)𝐁k,s(t,z(t))ΔaWt,s)(limΔtmax0Δbtπ(ti,ti+1)𝐁l,q(t,z(t))ΔbWt,q)]

Factor out the limit and swap it with the expected value (I think this works but not sure)

=limΔtmax0𝔼[sUqU(Δatπ(ti,ti+1)𝐁k,s(t,z(t))ΔaWt,s)(Δbtπ(ti,ti+1)𝐁l,q(t,z(t))ΔbWt,q)]
=limΔtmax0𝔼[sUqUΔatπ(ti,ti+1)Δbtπ(ti,ti+1)(𝐁k,s(t,z(t))ΔaWt,s)(𝐁l,q(t,z(t))ΔbWt,q)]

then rearange

=limΔtmax0sUqUΔatπ(ti,ti+1)Δbtπ(ti,ti+1)𝔼[(𝐁k,s(t,z(t))𝐁l,q(t,z(t)))(ΔaWt,sΔbWt,q)]

The (ΔaWt,sΔbWt,q) are are independent, so their covariance is always zero if sq. So in the sume we only keep the diagonals

=limΔtmax0sUΔatπ(ti,ti+1)Δbtπ(ti,ti+1)𝔼[(𝐁k,s(t,z(t))𝐁l,s(t,z(t)))(ΔaWt,sΔbWt,s)]

And the increments in (ΔaWt,sΔbWt,s) are also independent by definition of a Weiner process, so the expected value goes to zero for different increments ab

=limΔtmax0sUΔatπ(ti,ti+1)𝔼[(𝐁k,s(t,z(t))𝐁l,s(t,z(t)))(ΔaWt,sΔaWt,s)]

And the variance of a Weiner processes increment is by definition the increment of time, so (ΔaWt,sΔaWt,s)=Δat

=limΔtmax0sUΔatπ(ti,ti+1)𝔼[(𝐁k,s(t,z(t))𝐁l,s(t,z(t)))]Δat

And now its just the definition of a Reinmann integral

=sUtiti+1𝔼[(𝐁k,s(t,z(t))𝐁l,s(t,z(t)))]dt

Third and Fourth terms

So with

(titi+1Ak(t,z(t))dt)(titi+1𝐁l(t,z(t))dWt)

inside the summation it looks like

limΔtmax0i(titi+1Ak(t,z(t))dt)(titi+1𝐁l(t,z(t))dWt)

If we use Cauchy Schwarz inequality where i|AiBi|iAi2iBi2 then

limΔtmax0|i(titi+1Ak(t,z(t))dt)(titi+1𝐁l(t,z(t))dWt)|limΔtmax0i(titi+1Ak(t,z(t))dt)2(titi+1𝐁l(t,z(t))dWt)2

And we've seen that the first term goes to zero, and the second term is finite so

=0

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

limΔtmax0𝔼[ΔizkΔizl]=sUtiti+1𝔼[(𝐁k,s(t,z(t))𝐁l,s(t,z(t)))]dt

Looking back at the Taylor series if we take the expected value

𝔼[f(t+k,z(t+k))f(t,z(t))]=𝔼[limΔtmax0i(ftΔt+kUfzkΔizk+122f2tΔt2+12kUlU2fzkzlΔizkΔizl+kU2ftzkΔtΔizk+)]

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

=𝔼[limΔtmax0i(ftΔt+kUfzkΔizk+12kUlU2fzkzlΔizkΔizl)]
=limΔtmax0i(ftΔt+kU𝔼[fzkΔizk]+12kUlU𝔼[2fzkzlΔizkΔizl])

The first part is just a Reinmann integral

=tt+kftdtlimΔtmax0i(kU𝔼[fzkΔizk]+12kUlU𝔼[2fzkzlΔizkΔizl])

Substitute

=tt+kftdtlimΔtmax0i(kU𝔼[fzk(titi+1A(t,z(t))kdt+titi+1𝐁(t,z(t))kdWt)]+12kUlU𝔼[2fzkzlΔizkΔizl])

and adding the integrals just concatenates them so

=tt+kftdt+kU(tt+k𝔼[fzkA(t,z(t))k]dt+titi+1𝔼[𝐁(t,z(t))kdWt])+limΔtmax0i(12kUlU𝔼[2fzkzlΔizkΔizl])

The expected value of just the drift term is zero so

=tt+kftdt+kUtt+k𝔼[fzkA(t,z(t))k]dt+limΔtmax0i(12kUlU𝔼[2fzkzlΔizkΔizl])

Then if we substitute again

=tt+kftdt+kUtt+k𝔼[fzkA(t,z(t))k]dt+limΔtmax0i(12kUlU𝔼[2fzkzl(sUtiti+1(𝐁k,s(t,z(t))𝐁l,s(t,z(t)))dt)])

and the integrals just concatenate again so the limit doesn't matter

=tt+kftdt+kUtt+k𝔼[fzkA(t,z(t))k]dt+12kUlUtt+k𝔼[2fzkzl(sU(𝐁k,s(t,z(t))𝐁l,s(t,z(t))))]dt

Then let

𝐃=𝐁T𝐁

so then

=tt+kftdt+kUtt+k𝔼[fzkA(t,z(t))k]dt+12kUlUtt+k𝔼[2fzkzl𝐃k,l(t,z(t))]dt

So we get the formula expression

𝔼[f(t+k,z(t+k))f(t,z(t))]=tt+kftdt+kUtt+k𝔼[fzkA(t,z(t))k]dt+12kUlUtt+k𝔼[2fzkzl𝐃k,l(t,z(t))]dt

And since its deterministic you can do

ddt𝔼[f(t,z(t))]=𝔼[ft+kUfzkA(t,z(t))k+12kUlU2fzkzl𝐃k,l(t,z(t))]

final part

We also know that

𝔼[f(z(t))]=Vf(z)p(t,z)dz

so taking the derivative of both sides

ddt𝔼[f(z(t))]=Vf(z)dp(t,z)dtdz

and substituting

V(kUfzkA(t,z(t))k+12kUlU2fzkzl𝐃k,l(t,z(t)))p(t,z)dz=Vf(z)dp(t,z)dtdz

Divergence theorem

Split the integral up

=VkUfzkA(t,z(t))kp(t,z)dz+V12kUlU2fzkzl𝐃k,l(t,z(t))p(t,z)dz

first part

focusing on the first part

=VkUfzkA(t,z(t))kp(t,z)dz

We can write it like this

=VkU((zf)(Ap))dz

and remember that a scalar and a vector

(ab)=(ab)+(ab)

And with the divercence theorem

Vzc(z)dz=Ω(V)(c(z)n)dz

And if its two of then dotted then

V(zab)dz+V(azb)dz=Ω(V)((ab)n)dz

so

=Ω(V)((pAf)n)dzVfz(pA)dz

And if we assume the probability goes to zero at the edge (or in the limit to infinity) then the boundary condition dissapears

=Vfz(pA)dz

second part

TODO, just the same thing but twice

result

So we have

Vptfdz=V(kUzk(pA(t,z(t))k)+12kUlU2zkzl(p𝐃k,l(t,z(t))))f(t,z)dz

and since it works for arbitrary function f it must be that

pt=kUzk(pA(t,z(t))k)+12kUlU2zkzl(p𝐃k,l(t,z(t)))