On Markov chains associated to random systems with complete connection

1978 ◽  
Vol 10 (2) ◽  
pp. 295-296
Author(s):  
Thomas Kaijser
Keyword(s):  
Markov Chains ◽  
Random Systems ◽  
Complete Connection

On Markov chains associated to random systems with complete connection

1978 ◽  
Vol 10 (02) ◽  
pp. 295-296
Author(s):  
Thomas Kaijser
Keyword(s):  
Markov Chains ◽  
Random Systems ◽  
Complete Connection

scholarly journals Asymptotics of locally-interacting Markov chains with global signals

2002 ◽  
Vol 34 (2) ◽  
pp. 416-440
Author(s):  
Ulrich Horst
Keyword(s):  
Markov Chains ◽  
Random Systems ◽  
Infinite Product ◽  
Sufficient Conditions ◽  
Convergence Result ◽  
Microscopic Level ◽  
Product Spaces ◽  
Macroscopic Level ◽  
Long Run ◽  
Sufficient Conditions For Convergence

We study the long-run behaviour of interactive Markov chains on infinite product spaces. The behaviour at a single site is influenced by the local situation in some neighbourhood and by a random signal about the average situation throughout the whole system. The asymptotic behaviour of such Markov chains is analyzed on the microscopic level and on the macroscopic level of empirical fields. We give sufficient conditions for convergence on the macroscopic level. Combining a convergence result from the theory of random systems with complete connections with a perturbation of the Dobrushin-Vasserstein contraction technique, we show that macroscopic convergence implies that the underlying microscopic process has local asymptotic loss of memory.

scholarly journals Asymptotics of locally-interacting Markov chains with global signals

2002 ◽  
Vol 34 (02) ◽  
pp. 416-440 ◽  
Author(s):  
Ulrich Horst
Keyword(s):  
Markov Chains ◽  
Random Systems ◽  
Infinite Product ◽  
Sufficient Conditions ◽  
Convergence Result ◽  
Microscopic Level ◽  
Product Spaces ◽  
Macroscopic Level ◽  
Long Run ◽  
Sufficient Conditions For Convergence

We study the long-run behaviour of interactive Markov chains on infinite product spaces. The behaviour at a single site is influenced by the local situation in some neighbourhood and by a random signal about the average situation throughout the whole system. The asymptotic behaviour of such Markov chains is analyzed on the microscopic level and on the macroscopic level of empirical fields. We give sufficient conditions for convergence on the macroscopic level. Combining a convergence result from the theory of random systems with complete connections with a perturbation of the Dobrushin-Vasserstein contraction technique, we show that macroscopic convergence implies that the underlying microscopic process has local asymptotic loss of memory.

Empirical Markov chains as models of dynamic processes

2008 ◽  
Author(s):  
Stephen Merrill
Keyword(s):  
Markov Chains ◽  
Dynamic Processes

Markov models—Markov chains

2019 ◽  
Vol 16 (8) ◽  
pp. 663-664 ◽  
Author(s):  
Jasleen K. Grewal ◽  
Martin Krzywinski ◽  
Naomi Altman
Keyword(s):  
Markov Chains ◽  
Markov Models

SINGULAR SPECTRAIN ONE-DIMENSIONAL RANDOM SYSTEMS

1983 ◽  
Vol 44 (C3) ◽  
pp. C3-1555-C3-1556
Author(s):  
T. A.L. Ziman
Keyword(s):  
Random Systems ◽  
One Dimensional

Optimal and Near-Optimal Incentive Strategies in the Hierarchical Control of Markov Chains

1983 ◽  
Author(s):  
Vikram R. Saksena ◽  
J. B. Cruz
Keyword(s):  
Markov Chains ◽  
Hierarchical Control ◽  
Incentive Strategies

On Hankel-Norm Approximation of Large-Scale Markov Chains

1991 ◽  
Author(s):  
Guanrong Chen ◽  
Charles K. Chui ◽  
Yaoqi Yu
Keyword(s):  
Markov Chains ◽  
Large Scale ◽  
Hankel Norm ◽  
Norm Approximation
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