Convergence across Chinese provinces: An analysis using Markov transition matrix

2008 ◽  
Vol 19 (1) ◽  
pp. 66-79 ◽  
Author(s):  
Hiroshi SAKAMOTO ◽  
Nazrul ISLAM
Keyword(s):  
Transition Matrix ◽  
Markov Transition Matrix ◽  
Markov Transition

scholarly journals K-theory for Cuntz-Krieger algebras arising from real quadratic maps

2003 ◽  
Vol 2003 (34) ◽  
pp. 2139-2146 ◽  
Author(s):  
Nuno Martins ◽  
Ricardo Severino ◽  
J. Sousa Ramos
Keyword(s):  
Transition Matrix ◽  
Parameter Family ◽  
Quadratic Maps ◽  
Markov Transition Matrix ◽  
Kneading Sequence ◽  
Markov Transition ◽  
K Theory ◽  
The One ◽  
Real Quadratic

We compute theK-groups for the Cuntz-Krieger algebras𝒪A𝒦(fμ), whereA𝒦(fμ)is the Markov transition matrix arising from the kneading sequence𝒦(fμ)of the one-parameter family of real quadratic mapsfμ.

Constrained adaptive Markov transition matrix based target tracking with IMMPF

2013 ◽  
Vol 100 (11) ◽  
pp. 1569-1578
Author(s):  
Fei Wang ◽  
Mathini Sellathurai ◽  
David Wilcox ◽  
Jianjiang Zhou
Keyword(s):  
Target Tracking ◽  
Transition Matrix ◽  
Markov Transition Matrix ◽  
Markov Transition

scholarly journals Empirical Convergence Rate of a Markov Transition Matrix

2019 ◽  
pp. 1-7
Author(s):  
Steven T. Garren
Keyword(s):  
Convergence Rate ◽  
Confidence Intervals ◽  
Transition Matrix ◽  
Current Time ◽  
Computing Power ◽  
Largest Eigenvalue ◽  
Markov Transition Matrix ◽  
Time Period ◽  
Markov Transition ◽  
Second Largest Eigenvalue

The convergence rate of a Markov transition matrix is governed by the second largest eigenvalue, where the first largest eigenvalue is unity, under general regularity conditions. Garren and Smith (2000) constructed confidence intervals on this second largest eigenvalue, based on asymptotic normality theory, and performed simulations, which were somewhat limited in scope due to the reduced computing power of that time period. Herein we focus on simulating coverage intervals, using the advanced computing power of our current time period. Thus, we compare our simulated coverage intervals to the theoretical confidence intervals from Garren and Smith (2000).

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