Aperiodicity of a markov chain

In summary, the given transition matrix satisfies the definition of aperiodicity, as there exists a positive probability of transitioning between all states, regardless of whether n is odd or even.
  • #1
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my transition matrix is

0 0 1
0 0 1
(1/3) (2/3) 0

I'm supposed to argue that this chain is aperiodic,

A markov chain is aperiodic iff there exists a time n such that there is a positive probability of going from state i to state j for all i and j

This doesn't seem to hold for my chain ... for example, to go from state 1 to state 2 n has to be odd.. but to go from state 1 to state 1 or 3 n has to be even..

Am I just getting this definition muddled up? Could someone elaborate on it for me? Thanks
 
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  • #2
in advance!Yes, you are getting this definition muddled up. The definition of aperiodicity is that for all states i and j, there exists an n such that the probability of transitioning from i to j is positive. This means that it doesn't matter whether n is odd or even, there must exist some n such that the probability is positive. In your transition matrix, we can see that all states have a positive probability of transitioning to each other state. For example, the probability of transitioning from state 1 to state 2 is 0, but the probability of transitioning from state 1 to state 3 is 1/3 (which is positive). Similarly, the probability of transitioning from state 2 to state 3 is 2/3 (which is positive). Therefore, since all states have a positive probability of transitioning to each other, this chain is aperiodic.
 

1. What is aperiodicity of a Markov chain?

Aperiodicity refers to the property of a Markov chain where the probability of moving from one state to another is not dependent on time. In other words, the chain does not exhibit any repeating patterns or cycles.

2. How is aperiodicity different from periodicity in a Markov chain?

Aperiodicity and periodicity are two opposite properties of a Markov chain. While aperiodicity means that the chain does not exhibit any repeating patterns, periodicity implies that the chain has a fixed period or cycle length, where the probability of moving from one state to another is dependent on time.

3. What causes a Markov chain to be aperiodic?

A Markov chain can be aperiodic if it satisfies the condition of irreducibility, which means that there is a non-zero probability of transitioning from any state to any other state. It also requires that the chain is not absorbing, meaning there is a non-zero probability of eventually leaving any state.

4. How can we determine if a Markov chain is aperiodic?

One way to determine if a Markov chain is aperiodic is by calculating the greatest common divisor (GCD) of all possible cycle lengths of the chain. If the GCD is 1, then the chain is aperiodic. Another way is by checking if the chain satisfies the conditions of irreducibility and non-absorbing states.

5. What is the significance of aperiodicity in a Markov chain?

Aperiodicity is an important property of a Markov chain as it ensures that the chain will eventually reach any state with positive probability, and will not get stuck in any particular state. This allows for a more accurate representation of real-world systems and processes, where events may occur at irregular intervals.

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