Integral in Gardiner's book on stochastic methods

In summary, the conversation discusses a proof in the Handbook of Stochastic Methods by Gardiner that involves an integral and two solutions of the Chapman-Kolmogorov equation. Gardiner suggests taking one of the solutions as a stationary distribution, which allows the integral to be integrated into surface terms that vanish at infinity. However, the person is unsure how to prove that one of the solutions vanishes at infinity, as they only know that the other solution does. The book being referenced is the third edition, specifically chapter 3, section 3.7, and subsection 3.7.3.
  • #1
eoghan
207
7
Dear all,
I have troubles in one proof of the book Handbook of stochastic methods by Gardiner. In the paragraph 3.7.3 he writes this integral
[itex]\sum_i\int d\vec x \frac{\partial}{\partial x_i}[-A_ip_1\log(p_1/p_2)][/itex]
where [itex]p_1[/itex] and [itex]p_2[/itex] are two solutions of the Chapman-Kolmogorov equation and [itex]\vec A[/itex] is a function of [itex]\vec x[/itex]. Then Gardiner says, suppose that we take [itex]p_2[/itex] as a stationary distribution [itex]p_s(\vec x)[/itex] which is nonzero everywhere, except at infinity, where it and its first derivative vanish. The integral can be integrated to give surface terms which vanish at infinity.
I don't know how to prove this! I used the Gauss theorem to obtain:
[itex]\sum_i\int_D d\vec x \frac{\partial}{\partial x_i}[-A_ip_1\log(p_1/p_2)]=
-\int_{D} d\vec x \nabla[\vec A p_1\log(p_1/p_2)]=
-\int_{\partial D} dS \:\:\hat n\cdot[\vec A p_1\log(p_1/p_2)][/itex]

and this is a surface term, where the surface extends to infinity. Now I should conclude that [itex]p_1\log(p_1/p_2)[/itex] is zero at infinity, but I don't know how to proof that. I mean, I only know that [itex]p_2[/itex] is zero at infinity and this would make the integral to diverge! Maybe I can say that since [itex]p_1[/itex] it's solution to the Chapman-Kolmogorov equation, it is itself a distribution and so also [itex]p_1[/itex] vanishes at infinity, but I'm not sure about this.
 
  • #3
eoghan said:
Dear all,
I have troubles in one proof of the book Handbook of stochastic methods by Gardiner.

Not that I can answer your question, but which edition of Handbook of Stochastic Methods is involved? There is a "section" 3.7.3 in the second edition, but I don't see the equation you mention.
 
  • #4
Stephen Tashi said:
Not that I can answer your question, but which edition of Handbook of Stochastic Methods is involved? There is a "section" 3.7.3 in the second edition, but I don't see the equation you mention.
Hi Stephen!
The book is the third edition. Chapter 3 = Markov Processes, section 3.7=Stationary and Homogeneous Markov Processes, subsection 3.7.3=Approach to a Stationary Process
 

1. What is an integral in Gardiner's book on stochastic methods?

An integral in Gardiner's book on stochastic methods refers to the mathematical concept of finding the area under a curve. In the context of stochastic methods, it is used to calculate the expected value of a random variable over a specific time period.

2. Why is the integral important in Gardiner's book on stochastic methods?

The integral is important in Gardiner's book on stochastic methods because it allows for the calculation of important quantities such as expected values and probabilities in the context of stochastic processes. It also provides a way to model and analyze random phenomena in various fields such as finance, physics, and biology.

3. What are the different types of integrals discussed in Gardiner's book on stochastic methods?

Gardiner's book on stochastic methods covers various types of integrals, including Riemann integrals, Lebesgue integrals, and Itô integrals. Each type has its own applications and uses in the study of stochastic processes.

4. How does Gardiner's book on stochastic methods explain the use of integrals in modeling random phenomena?

Gardiner's book on stochastic methods explains the use of integrals in modeling random phenomena through the concept of stochastic calculus. This involves the integration of stochastic processes, which are mathematical models of random events, to analyze and predict the behavior of these events over time.

5. Are there any limitations to the use of integrals in Gardiner's book on stochastic methods?

While integrals are a powerful tool in the study of stochastic processes, they do have some limitations. For example, they may not be applicable to all types of stochastic processes, and certain assumptions must be made in order for them to be used effectively. Gardiner's book discusses these limitations and provides alternative methods for dealing with them.

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