Master equation for Ornstein-Uhlenbeck process

In summary: Gaussian centered at y2=0 with variance 1.In summary, the conversation discusses the formulation of the Ornstein-Uhlenbeck process as a master equation. The transition probability and probability distribution are defined, and the difficulty in defining the master equation for small values of t is mentioned. The conversation concludes with a suggestion to use a Gaussian distribution with variance 1 as t -> 0.
  • #1
cjolley
3
0
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 [itex]y_{1}[/itex] and [itex]y_{2}[/itex] separated by a time t:

[itex]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})})[/itex]

and the probability distribution

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

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

A little bit later, he defines the master equation by expanding T[itex]_{t}(y_{2}|y_{1})[/itex] in powers of t and defining the coefficient of the linear term as [itex]W(y_{2}|y_{1})[/itex], 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[itex]_{t}(y_{2}|y_{1})[/itex]→δ[itex](y_{2}-y_{1})[/itex], since T[itex]_{t}(y_{2}|y_{1})[/itex] 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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  • #2
For small t, 1-e-2t ≈ 2t. This gives a Gaussian with variance ≈ 2t, which -> delta function as t -> 0.
 
  • #3
Exactly. But that still doesn't tell me how to get a small-t expansion for [itex]y_{1} ≠ y_{2}[/itex], which is what I need for a meaningful transition rate.
 
  • #4
The point is that [itex]y[/itex] 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.
 
  • #5
e-t≈1-t, so y1-y2e-t≈y1-y2-ty2. The net result for the integrand as t -> 0 is δ( y1-y2)
 

1. What is the Master Equation for the Ornstein-Uhlenbeck process?

The Master Equation for the Ornstein-Uhlenbeck process is a mathematical formula that describes the time evolution of a stochastic process, which is a random process that changes over time. It is commonly used in physics and finance to model the behavior of a wide range of systems, including Brownian motion and stock prices.

2. How is the Master Equation derived?

The Master Equation for the Ornstein-Uhlenbeck process is derived from the Fokker-Planck equation, which is a partial differential equation that describes the probability density function of a stochastic process. By applying the Fokker-Planck equation to the Ornstein-Uhlenbeck process, we can obtain the Master Equation.

3. What are the key assumptions of the Master Equation for the Ornstein-Uhlenbeck process?

The Master Equation for the Ornstein-Uhlenbeck process assumes that the process is Markovian, meaning that the future behavior of the process depends only on its current state and not on its past history. It also assumes that the process is stationary, meaning that its statistical properties do not change over time. Additionally, it assumes that the process is Gaussian, meaning that its probability distribution follows a normal distribution.

4. What is the significance of the Master Equation for the Ornstein-Uhlenbeck process?

The Master Equation for the Ornstein-Uhlenbeck process is significant because it provides a powerful tool for analyzing and predicting the behavior of stochastic processes. It allows us to calculate important quantities such as the mean and variance of the process, as well as the probability of the process reaching a certain value in a given time frame. This has applications in a variety of fields, including physics, economics, and biology.

5. Are there any limitations to the Master Equation for the Ornstein-Uhlenbeck process?

Yes, there are some limitations to the Master Equation for the Ornstein-Uhlenbeck process. It assumes that the process is driven by random noise, and does not take into account any external factors that may influence the process. It also assumes that the process is continuous, which may not always be the case in real-world systems. Additionally, the Master Equation may be difficult to solve for complex systems, requiring numerical methods to approximate the solution.

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