Master equation for Ornstein-Uhlenbeck process

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The discussion revolves around the formulation of the Ornstein-Uhlenbeck process and its master equation as defined in N.G. Van Kampen's "Stochastic Processes in Physics and Chemistry." The transition probability T_{t}(y_{2}|y_{1}) approaches a delta function as time t approaches zero, complicating the small-t expansion needed for defining the master equation. Participants highlight that the Ornstein-Uhlenbeck process can be related to the Fokker-Planck equation, which describes the time evolution of the probability distribution. A suggestion is made to consider the Langevin equation, which incorporates Gaussian noise, as a means to derive the necessary transition rates. The conversation emphasizes the need for a clear understanding of the small-t behavior of the process to establish a meaningful transition probability.
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Hi everybody...

I've been working through N.G. Van Kampen's "Stochastic Processes in Physics and Chemistry" and have run into something that has got me sort of stumped. He defines the Ornstein-Uhlenbeck process (a stationary, Gaussian, Markovian random process) in terms of the transition probability between two values y_{1} and y_{2} separated by a time t:

T_{t}(y_{2}|y_{1}) = (2π(1-e^{-2t})^{1/2}\exp(\frac{(y_{2}-y_{1}e^{-t})^{2}}{2(1-e^{-2t})})

and the probability distribution

P_{1}(y_{1}) = (2π)^{-1/2}e^{-y_{1}^{2}/2}

(This is pg. 83 if you have the book.)

A little bit later, he defines the master equation by expanding T_{t}(y_{2}|y_{1}) in powers of t and defining the coefficient of the linear term as W(y_{2}|y_{1}), the transition probability per unit time (pg. 96 if you have the book).

The thing that's got me stuck here is that, as t→0, T_{t}(y_{2}|y_{1})→δ(y_{2}-y_{1}), since T_{t}(y_{2}|y_{1}) is a Gaussian. I can't seem to come up with a reasonable way to expand this in terms of small t, which makes it difficult to define the master equation.

So... does anyone know how the Ornstein-Uhlenbeck process is formulated as a master equation? Any ideas would be greatly appreciated.

--craig
 
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For small t, 1-e-2t ≈ 2t. This gives a Gaussian with variance ≈ 2t, which -> delta function as t -> 0.
 
Exactly. But that still doesn't tell me how to get a small-t expansion for y_{1} ≠ y_{2}, which is what I need for a meaningful transition rate.
 
The point is that y is a random variable. I don't know that book, but what he finally should get at, I guess, is that the Fokker-Planck equation for the time evolution of the distribution function is equivalent to a Langevin equation, which is an ordinary stochastic differential equation with Gaussian-distributed (white) noise.

For a simple derivation (however for the relativistic case) see

http://fias.uni-frankfurt.de/~hees/publ/hq-qgp4-bibtex.pdf

p. 41ff.
 
e-t≈1-t, so y1-y2e-t≈y1-y2-ty2. The net result for the integrand as t -> 0 is δ( y1-y2)
 
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