Concept question on irreducibility (markov chain)

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  • #1
hsinyihhsu
1
0

Homework Statement


This is not really a homework question but just something that I'm confused about.

I'm having trouble with understanding the movement of Markov chain.
Say, if S = {0,1,2...} and 0<q<1. Let p(i,i+1)=q and p(i,i-1)=1-q. I can see how this Markov chain moves (back and forth, basically; going one step at a time from time t to time t+1 with probability of either q or 1-q). I can see that this Markov chain is irreducible, since if i<j, then p(i,j) = q^(j-i)>0 and p(j,i) = 1-q^(j-1)>0 (by the way, is this explanation right?)

However, what about for the Markov chain that looks like this:
S = {0,1,2...} and 0<q<1. Let p(i,i+1)=q and p(i,0)=1-q? How does it move? for p(i,i+1)=q it is going incrementally one step at a time, but how about p(i,0)=1-q? Does it skip all the way back to 0? How can I then tell whether if this Markov chain is irreducible or not?

Any help would be appreciated. Thanks in advance!
 
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  • #2
hsinyihhsu said:

Homework Statement


This is not really a homework question but just something that I'm confused about.

I'm having trouble with understanding the movement of Markov chain.
Say, if S = {0,1,2...} and 0<q<1. Let p(i,i+1)=q and p(i,i-1)=1-q. I can see how this Markov chain moves (back and forth, basically; going one step at a time from time t to time t+1 with probability of either q or 1-q). I can see that this Markov chain is irreducible, since if i<j, then p(i,j) = q^(j-i)>0 and p(j,i) = 1-q^(j-1)>0 (by the way, is this explanation right?)

However, what about for the Markov chain that looks like this:
S = {0,1,2...} and 0<q<1. Let p(i,i+1)=q and p(i,0)=1-q? How does it move? for p(i,i+1)=q it is going incrementally one step at a time, but how about p(i,0)=1-q? Does it skip all the way back to 0? How can I then tell whether if this Markov chain is irreducible or not?

Any help would be appreciated. Thanks in advance!

Starting from state 0, is possible to reach state i > 1 for any given i (that is, is there a non-zero probability of reaching state i eventually)? Starting from state 0, is it possible to return to state 0 (at some finite time in the future)? If your answer is YES to both these questions, what would that tell you about the markov chain?

RGV
 

What is the definition of irreducibility in a Markov chain?

Irreducibility in a Markov chain refers to the property of the chain where every state can be reached from any other state with a non-zero probability. In other words, there are no isolated or unreachable states in the chain.

How does irreducibility affect the long-term behavior of a Markov chain?

If a Markov chain is irreducible, it means that there is a possibility for the chain to return to any state in the long run, regardless of its starting state. This leads to a steady-state distribution where the probabilities of being in each state remain constant over time.

Can a Markov chain be reducible and irreducible at the same time?

No, a Markov chain can only be either reducible or irreducible. If a chain is reducible, it means that there are two or more subsets of states that are not connected, making it impossible to reach certain states from others. In contrast, irreducible chains have only one subset of states, which are all connected.

How can irreducibility be determined in a Markov chain?

There are several methods for determining irreducibility in a Markov chain. One way is to construct a transition probability matrix and check if there are any rows or columns that contain all zeros, indicating unreachable states. Another method is to simulate the chain and observe if it is possible to move between all states.

What are the implications of irreducibility in practical applications of Markov chains?

Irreducibility is an important property in Markov chains as it allows for the prediction of long-term behavior and steady-state probabilities. It is also necessary for the existence of a unique stationary distribution, which is often used to model real-world systems such as stock market trends, weather patterns, and population dynamics.

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