The stability of conditional Markov processes and Markov chains in random environments

The Stability of Conditional Markov Processes and Markov Chains in Random Environments

We consider a discrete time hidden Markov model where the signal is a stationary Markov chain. When conditioned on the observations, the signal is a Markov chain in a random environment under the conditional measure. It is shown that this conditional signal is weakly ergodic when the signal is ergodic and the observations are nondegenerate. This permits a delicate exchange of the intersection a...

Stability and Approximation of Random Invariant Measures of Markov Chains in Random Environments

We consider finite-state Markov chains driven by a P-stationary ergodic invertible process σ : Ω → Ω, representing a random environment. For a given initial condition ω ∈ Ω, the driven Markov chain evolves according to A(ω)A(σω) · · ·A(σn−1), where A : Ω → Md is a measurable d × d stochastic matrix-valued function. The driven Markov chain possesses P-a.e. a measurable family of probability vect...

Empirical Bayes Estimation in Nonstationary Markov chains

Estimation procedures for nonstationary Markov chains appear to be relatively sparse. This work introduces empirical  Bayes estimators  for the transition probability  matrix of a finite nonstationary  Markov chain. The data are assumed to be of  a panel study type in which each data set consists of a sequence of observations on N>=2 independent and identically dis...

Stability and approximation of invariant measures of Markov chains in random environments

We consider finite-state Markov chains driven by stationary ergodic invertible processes representing random environments. Our main result is that the invariant measures of Markov chains in random environments (MCREs) are stable under a wide variety of perturbations. We prove stability in the sense of convergence in probability of the invariant measure of the perturbed MCRE to the original inva...

The KTH Visit in Semi-Markov Processes