Steady-state vector of infinite markov chain

In summary, Today we encountered a problem with markov chains and are wondering if this can be solved analytically. We discussed a banded transition probability matrix with a specific pattern and how it is impossible to construct a stochastic matrix with finite size unless a certain element is set to a specific value. We also explored how calculating the eigenvectors or raising the matrix to high powers can provide a good approximation of the solution. Additionally, we talked about the sum of the steady-state vector elements and if it can be expanded into a series and if its limit values are known. Finally, we learned that the steady-state probabilities can be found by solving a recurrence relation and that the solution is a geometric series.
  • #1
pokey909
1
0
Today we encountered a problem with markov chains and are wondering if this can be solved analytically.

Suppose we have a banded transition probability matrix M of the following form:
M=
[
P P 0 0 0 ...
Q 0 P 0 0 ...
0 Q 0 P 0 ...
0 0 Q 0 P ...
0 0 0 Q 0 ...
. . . . .
. . . . . ]

So with the exception of column 1, all others are equal. The "P 0 Q" element is only shifted one down (or row-wise, "Q 0 P" is shifted to the right).
It is impossible to construct a stochastic matrix with finite size unless you set the bottom-right element to Q.
When this is done, a good approximation of the real solution can be obtained by calculating the eigenvectors of the matrix or just raising it ot high powers.

Generally I am only interested in the sum of the steady-state vector elements.
So I am wondering if the evolution of M^n with n goin from 1 to infinity can be expanded into a series for which limit values are known.

Does anyone know a way to exactly calculate the limit value of the sum of the steady-state vector?
 
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  • #2
The steady-state probabilities can be found by solving Mx=x. For your example this produces a recurrence relation whose solution is a geometric series.
 

Related to Steady-state vector of infinite markov chain

1. What is a steady-state vector in an infinite Markov chain?

A steady-state vector in an infinite Markov chain is a probability distribution that represents the long-term behavior of the system. It contains the probabilities of being in each state after an infinite number of transitions.

2. How is the steady-state vector calculated?

The steady-state vector can be calculated by solving a system of linear equations, where each equation represents the balance between the probabilities of transitioning into and out of a state. The solution to this system of equations is the steady-state vector.

3. Can a steady-state vector be found for any infinite Markov chain?

No, a steady-state vector can only be found for a finite, irreducible, and aperiodic Markov chain. If the chain is not irreducible, meaning that there are unreachable states, or if it is periodic, meaning that certain states can only be reached after a certain number of transitions, then a steady-state vector does not exist.

4. What is the significance of the steady-state vector in an infinite Markov chain?

The steady-state vector is important because it provides insights into the long-term behavior of the system. It can help predict the probabilities of being in each state after a large number of transitions, and can also be used to calculate other properties of the system, such as expected values and steady-state probabilities.

5. How is the steady-state vector used in practical applications?

The steady-state vector is commonly used in various fields, such as finance, biology, and engineering, to model and analyze real-world systems. It can be used to make predictions about the future behavior of a system, identify optimal strategies, and evaluate the performance of different systems. It is also a fundamental concept in the study of stochastic processes and probability theory.

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