A decomposition theorem for infinite stochastic matrices

1995 ◽  
Vol 32 (4) ◽  
pp. 893-901 ◽  
Author(s):  
Daniel P. Heyman

We prove that every infinite-state stochastic matrix P say, that is irreducible and consists of positive-recurrrent states can be represented in the form I – P=(A – I)(B – S), where A is strictly upper-triangular, B is strictly lower-triangular, and S is diagonal. Moreover, the elements of A are expected values of random variables that we will specify, and the elements of B and S are probabilities of events that we will specify. The decomposition can be used to obtain steady-state probabilities, mean first-passage-times and the fundamental matrix.

1995 ◽  
Vol 32 (04) ◽  
pp. 893-901 ◽  
Author(s):  
Daniel P. Heyman

We prove that every infinite-state stochastic matrix P say, that is irreducible and consists of positive-recurrrent states can be represented in the form I – P=(A – I)(B – S), where A is strictly upper-triangular, B is strictly lower-triangular, and S is diagonal. Moreover, the elements of A are expected values of random variables that we will specify, and the elements of B and S are probabilities of events that we will specify. The decomposition can be used to obtain steady-state probabilities, mean first-passage-times and the fundamental matrix.


2020 ◽  
Vol 152 (10) ◽  
pp. 104108 ◽  
Author(s):  
Adam Kells ◽  
Vladimir Koskin ◽  
Edina Rosta ◽  
Alessia Annibale

2013 ◽  
Vol 7 (1) ◽  
pp. 130 ◽  
Author(s):  
Mieczyslaw Torchala ◽  
Przemyslaw Chelminiak ◽  
Michal Kurzynski ◽  
Paul A Bates

2007 ◽  
Author(s):  
Christian H. Jensen ◽  
Dmitry Nerukh ◽  
Robert C. Glen ◽  
Arno P. J. M. Siebes ◽  
Michael R. Berthold ◽  
...  

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