Can Monte Carlo Methods be Used to Solve Infinite-State Integral Equations?

  • Thread starter Karlisbad
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In summary, the conversation discusses the C-K equation in integral form and the possibility of a differential version, as well as the use of Markov chains and Monte Carlo methods to solve systems of equations and integral equations with infinite or countably infinite states. The person suggests looking for more information on these topics through a Yahoo search and possibly through the book "Optimization of Weighted Monte Carlo Methods" by G.A.Mikhailov.
  • #1
Karlisbad
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If we have the C-K equation in the form (wikipedia :rolleyes: ):

[tex] p_{i_3;i_1}(f_3\mid f_1)=\int_{-\infty}^\infty p_{i_3;i_2}(f_3\mid f_2)p_{i_2;i_1}(f_2\mid f_1)df_2 [/tex]

this is the form of some kind of integral equation.. but is there any differential version of it?? (Chapman-Kolmogorov law into a differential form)

By the way i read that you could use a Markov chain (Particle with a finite number of transition states ) to solve by Montecarlo's method the system of equations

[tex] a_{j}+x_{j}=A_{i,j}x_{j} [/tex]

where we must find the x_j and the a_j are known numbers..

the obvious question is..can it be generalized for an infinite number of states to solve Integral equations with K(x,y)=K(y,x):

[tex] f(x)+g(x)=\int_{-\infty}^{\infty}K(x,y)f(x)dx [/tex]

in this last case i was thinking of a process with an infinite number of states, in the 2 cases:

a) the set of infinite states is numerable so [tex] {f1,f2,f3,f4,...} [/tex]

b) the set is Non-numerable.. (you can't label them)..:frown:

In these cases i would like to know if there're any applications of MOntecarlos method to solve systems or Integral equations..thanks.:redface: :shy:
 
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  • #3
Regarding your last sentence, you might investigate the book by G.A.Mikhailov titled
"Optimization of Weighted Monte Carlo Methods".
The originial is in Russian, but I believe it's been translated to English.
I don't own the book so I can't say precisely what's in it, but it's my understanding that it
considers (among other topics) statistical models (in this case vector Monte Carlo algorithms) for
solving systems described by integral equations.
 

What is the Chapmann-Kolmogorov equation?

The Chapmann-Kolmogorov equation is a fundamental equation in the field of stochastic processes, which describes the probability of transitioning from one state to another in a system that evolves over time.

What is the significance of the Chapmann-Kolmogorov equation?

The Chapmann-Kolmogorov equation is important because it allows us to model and analyze complex systems that involve randomness and uncertainty, such as stock prices, weather patterns, and biological processes.

How is the Chapmann-Kolmogorov equation derived?

The Chapmann-Kolmogorov equation is derived from the Markov property, which states that the future behavior of a system depends only on its current state, not on its past history. It is also derived from the principles of conditional probability and Bayes' rule.

What are the assumptions of the Chapmann-Kolmogorov equation?

The Chapmann-Kolmogorov equation assumes that the system in question is a Markov process, meaning that the probability of transitioning from one state to another is independent of the previous state. It also assumes that the system is time-homogeneous, meaning that the transition probabilities do not change over time.

How is the Chapmann-Kolmogorov equation applied in real-world scenarios?

The Chapmann-Kolmogorov equation is used in a variety of fields, such as finance, biology, and physics, to model and analyze complex systems with random and uncertain behavior. It is also used in machine learning and data analysis to make predictions and forecasts based on historical data.

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