Transition Matrix ( Markov Chain Monte Carlo)

In summary, the conversation discusses finding a regular transition matrix that is not time reversible, and the understanding that some power of the matrix must have all positive entries. It is suggested to try random regular transition matrices, as they are unlikely to be reversible. The concept of Pij=0≠Pji for some i,j is also discussed.
  • #1
mjt042
9
0
1. -Find a regular transition matrix that is not time reversible, i.e., doesn't satisfy the
balance equations?

2.Pi,j=0≠Pj,ifor some i and j
My understanding from Markov Chain Monte Carlo is that for the transition matrix to be regular the matrix has to have all positives entries and each row will add up to one. I was thinking the trick to this problem for it not satisfy the balance equation would be to take the transpose of the transition matrix. I was hoping someone could give me a hint if I am on the right track of thinking and where to go from there.
Thanks
 
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  • #2
mjt042 said:
1. -Find a regular transition matrix that is not time reversible, i.e., doesn't satisfy the
balance equations?




2.Pi,j=0≠Pj,ifor some i and j



My understanding from Markov Chain Monte Carlo is that for the transition matrix to be regular the matrix has to have all positives entries and each row will add up to one. I was thinking the trick to this problem for it not satisfy the balance equation would be to take the transpose of the transition matrix. I was hoping someone could give me a hint if I am on the right track of thinking and where to go from there.
Thanks

Your understanding is incorrect: P can be regular and have lots of zero entries. The regularity requirement is that some power ##P^n## has all positive entries. Also, your equation ##P_{ij} = 0 \neq P_{ji}## for some ##i,j## is likely not enough, nor is it needed.

You will probably get nowhere by taking the transpose of a transition matrix, since that will rarely give back a transition matrix again; if it does we call the transition matrix "doubly stochastic", and such transition matrices are rare.

Why not just try some more-or-less random (regular) transition matrices? They are unlikely to be reversible.
 
  • #3
Thanks for your help.
 
  • #4
I am still a little confused on how a regular transition matrix could not be reversible. Also what is meant by the statement Pij=0≠Pji for some i,j. Thanks
 

FAQ: Transition Matrix ( Markov Chain Monte Carlo)

What is a Transition Matrix in the context of Markov Chain Monte Carlo?

A Transition Matrix in Markov Chain Monte Carlo is a square matrix that represents the probabilities of moving from one state to another in a Markov Chain. Each row and column in the matrix represents a state, and the values in the matrix represent the probabilities of transitioning from one state to another in a single step. This matrix is a crucial component in simulating a Markov Chain through Monte Carlo methods.

How is a Transition Matrix used in Markov Chain Monte Carlo simulations?

In Markov Chain Monte Carlo simulations, a Transition Matrix is used to determine the next state in the Markov Chain based on the current state. This is done by multiplying the current state vector by the Transition Matrix, which results in a new state vector. This process is repeated multiple times to generate a sequence of states that approximate the distribution of interest.

What are the key properties of a Transition Matrix in Markov Chain Monte Carlo?

A Transition Matrix in Markov Chain Monte Carlo must have the following key properties:

  • The values in each row must sum to 1, representing the probabilities of transitioning to all possible states from the current state.
  • The values in each column must be between 0 and 1, representing the probabilities of transitioning to a specific state from all possible current states.
  • The matrix must be square, with the same number of rows and columns representing the same number of states.

How is a Transition Matrix constructed for a specific Markov Chain?

To construct a Transition Matrix for a specific Markov Chain, the probabilities of transitioning from one state to another must be known. These probabilities can be estimated from data or determined based on expert knowledge. Once the probabilities are determined, they are organized into a square matrix with the properties mentioned above. The size and structure of the matrix will depend on the number of states in the Markov Chain.

What are some applications of Transition Matrix in Markov Chain Monte Carlo?

Transition Matrix in Markov Chain Monte Carlo has various applications in different fields. Some of the common applications include:

  • Forecasting future trends and patterns in economics, finance, and business.
  • Analyzing customer behavior and predicting future purchasing patterns in marketing and sales.
  • Studying the spread of diseases and epidemics in epidemiology and public health.
  • Modeling gene sequences and protein structures in bioinformatics and genetics.

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