Stationary Probability Vector of a Higher-order Markov Chain By Zhang Shixiao Supervisors: Prof. Chi-Kwong Li and Dr. Jor-Ting Chan.

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Presentation transcript:

Stationary Probability Vector of a Higher-order Markov Chain By Zhang Shixiao Supervisors: Prof. Chi-Kwong Li and Dr. Jor-Ting Chan

Content 1. Introduction: Background 1. Introduction: Background 2. Higher-order Markov Chain 2. Higher-order Markov Chain 3. Conclusion 3. Conclusion

1. Introduction: Background Matrices are widely used in both science and engineering. In statistics Stochastic process: flow direction of a particular system or process. Stationary distribution: limiting behavior of a stochastic process.

Discrete Time-Homogeneous Markov Chains A stochastic process with a discrete finite state space S A unit sum vector X is said to be a stationary probability distribution of a finite Markov Chain if PX=X where

Discrete Time-Homogeneous Markov Chains

2. Higher-order Markov Chain Definition 2.1 Suppose the probability independent of time satisfying

2. Higher-order Markov Chain

Conditions for Infinitely Many Solutions over the Simplex

Then one of the following holds Otherwise, we must have a unique solution.

Conditions for Infinitely Many Solutions over the Simplex

Main Theorem 2.2

Other Given any two solutions lying on the interior of 1-dimensional face of the boundary of the simplex, then the whole 1-dimensional face must be a set of collection of solutions to the above equation. Conjecture: given any k+1 solutions lying in the interior of the k-dimensional face of the simplex, then any point lying in the whole k-dimensional face, including the vertexes and boundaries, will be a solution to the equation.

3. Conclusion

Thank you!