The Invariant Measures of Markov Chains on Product Spaces and a Measurement of Dependence

2011 ◽  
Vol 29 (3) ◽  
pp. 353-374
Author(s):  
Kensuke Makita
Keyword(s):  
Markov Chains ◽  
Invariant Measures ◽  
Product Spaces

Truncation approximations of invariant measures for Markov chains

1998 ◽  
Vol 35 (03) ◽  
pp. 517-536 ◽  
Author(s):  
R. L. Tweedie
Keyword(s):  
Markov Chain ◽  
Probability Distribution ◽  
Markov Chains ◽  
Transition Matrix ◽  
Invariant Measures ◽  
Invariant Distribution ◽  
Special Cases ◽  
Recurrent Markov Chain ◽  
Positive Recurrent

Let P be the transition matrix of a positive recurrent Markov chain on the integers, with invariant distribution π. If (n) P denotes the n x n ‘northwest truncation’ of P, it is known that approximations to π(j)/π(0) can be constructed from (n) P, but these are known to converge to the probability distribution itself in special cases only. We show that such convergence always occurs for three further general classes of chains, geometrically ergodic chains, stochastically monotone chains, and those dominated by stochastically monotone chains. We show that all ‘finite’ perturbations of stochastically monotone chains can be considered to be dominated by such chains, and thus the results hold for a much wider class than is first apparent. In the cases of uniformly ergodic chains, and chains dominated by irreducible stochastically monotone chains, we find practical bounds on the accuracy of the approximations.

Matrix-geometric invariant measures for G/M/l type Markov chains

1998 ◽  
Vol 14 (3) ◽  
pp. 537-569 ◽  
Author(s):  
H. R. Gail ◽  
S. L. Hantler ◽  
B. A. Taylor
Keyword(s):  
Markov Chains ◽  
Invariant Measures ◽  
Geometric Invariant

Markov chains and generalized continued fractions

1992 ◽  
Vol 29 (04) ◽  
pp. 838-849 ◽  
Author(s):  
Thomas Hanschke
Keyword(s):  
Markov Chains ◽  
Discrete Time ◽  
Wide Class ◽  
Stochastic Models ◽  
Continuous Time ◽  
Continued Fractions ◽  
Invariant Measures ◽  
Continuous Time Markov Chains ◽  
Generalized Continued Fractions

This paper deals with a class of discrete-time Markov chains for which the invariant measures can be expressed in terms of generalized continued fractions. The representation covers a wide class of stochastic models and is well suited for numerical applications. The results obtained can easily be extended to continuous-time Markov chains.

Invariant measures for Markov chains with no irreducibility assumptions

1988 ◽  
Vol 25 (A) ◽  
pp. 275-285 ◽  
Author(s):  
R. L. Tweedie
Keyword(s):  
Probability Theory ◽  
Invariant Measure ◽  
Markov Chains ◽  
Invariant Measures ◽  
Random Coefficient ◽  
General Function ◽  
Autoregressive Processes ◽  
Detailed Consideration ◽  
Positive Recurrence ◽  
Countable Space

Foster's criterion for positive recurrence of irreducible countable space Markov chains is one of the oldest tools in applied probability theory. In various papers in JAP and AAP it has been shown that, under extensions of irreducibility such as ϕ -irreducibility, analogues of and generalizations of Foster's criterion give conditions for the existence of an invariant measure π for general space chains, and for π to have a finite f-moment ∫π (dy)f(y), where f is a general function. In the case f ≡ 1 these cover the question of finiteness of π itself. In this paper we show that the same conditions imply the same conclusions without any irreducibility assumptions; Foster's criterion forces sufficient and appropriate regularity on the space automatically. The proofs involve detailed consideration of the structure of the minimal subinvariant measures of the chain. The results are applied to random coefficient autoregressive processes in order to illustrate the need to remove irreducibility conditions if possible.

Invariant measures for Markov chains with no irreducibility assumptions

1988 ◽  
Vol 25 (A) ◽  
pp. 275-285 ◽  
Author(s):  
R. L. Tweedie
Keyword(s):  
Probability Theory ◽  
Invariant Measure ◽  
Markov Chains ◽  
Invariant Measures ◽  
Random Coefficient ◽  
General Function ◽  
Autoregressive Processes ◽  
Detailed Consideration ◽  
Positive Recurrence ◽  
Countable Space

Foster's criterion for positive recurrence of irreducible countable space Markov chains is one of the oldest tools in applied probability theory. In various papers in JAP and AAP it has been shown that, under extensions of irreducibility such as ϕ -irreducibility, analogues of and generalizations of Foster's criterion give conditions for the existence of an invariant measure π for general space chains, and for π to have a finite f-moment ∫π (dy)f(y), where f is a general function. In the case f ≡ 1 these cover the question of finiteness of π itself.In this paper we show that the same conditions imply the same conclusions without any irreducibility assumptions; Foster's criterion forces sufficient and appropriate regularity on the space automatically. The proofs involve detailed consideration of the structure of the minimal subinvariant measures of the chain.The results are applied to random coefficient autoregressive processes in order to illustrate the need to remove irreducibility conditions if possible.

Markov chains and generalized continued fractions

1992 ◽  
Vol 29 (4) ◽  
pp. 838-849 ◽  
Author(s):  
Thomas Hanschke
Keyword(s):  
Markov Chains ◽  
Discrete Time ◽  
Wide Class ◽  
Stochastic Models ◽  
Continuous Time ◽  
Continued Fractions ◽  
Invariant Measures ◽  
Continuous Time Markov Chains ◽  
Generalized Continued Fractions

This paper deals with a class of discrete-time Markov chains for which the invariant measures can be expressed in terms of generalized continued fractions. The representation covers a wide class of stochastic models and is well suited for numerical applications. The results obtained can easily be extended to continuous-time Markov chains.

scholarly journals On the Computation of Invariant Measures in Random Dynamical Systems

2003 ◽  
Vol 03 (02) ◽  
pp. 247-265 ◽  
Author(s):  
Peter Imkeller ◽  
Peter Kloeden
Keyword(s):  
Dynamical Systems ◽  
Markov Chains ◽  
State Space ◽  
Difference Equations ◽  
Invariant Measures ◽  
Random Dynamical Systems ◽  
Continuum State ◽  
Transition Mechanism ◽  
Definition Of ◽  
Stationary Random Processes

Invariant measures of dynamical systems generated e.g. by difference equations can be computed by discretizing the originally continuum state space, and replacing the action of the generator by the transition mechanism of a Markov chain. In fact they are approximated by stationary vectors of these Markov chains. Here we extend this well-known approximation result and the underlying algorithm to the setting of random dynamical systems, i.e. dynamical systems on the skew product of a probability space carrying the underlying stationary stochasticity and the state space, a particular non-autonomous framework. The systems are generated by difference equations driven by stationary random processes modelled on a metric dynamical system. The approximation algorithm involves spatial discretizations and the definition of appropriate random Markov chains with stationary vectors converging to the random invariant measure of the system.

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