We state and prove new properties about Doeblin’s ergodicity coefficient for finite Markov chains. We show that this coefficient satisfies a sub-multiplicative type inequality (analogous to the Markov-Dobrushin’s ergodicity coefficient), and provide a novel but elementary proof of Doeblin’s characterization of weak-ergodicity for non-homogeneous chains. Using Doeblin’s coefficient, we illustrat...

A criterion of joint ergodicity of several sequences of transformations of a probability measure space X of the form T φi(n) i is given for the case where Ti are commuting measure preserving transformations of X and φi are integer valued generalized linear functions, that is, the functions formed from conventional linear functions by an iterated use of addition, multiplication by constants, and...

We consider the time-inhomogeneous Prendiville model with failures and repairs. The property of weak ergodicity is considered, estimates rate convergence for main probabilistic characteristics are obtained. Several examples considered showing how such obtained limiting themselves constructed.

Introduction. The question of ergodicity of a semigroup of bounded linear operators on a Banach space has been reduced, by Alaoglu and Birkhoff [l],1 Day [2, 3], and Eberlein [4], to the study firstly of the ergodicity of the semigroup itself and secondly, of the ergodicity of each element of the Banach space with respect to this ergodic semigroup. In the case of a bounded and commutative semig...

The paper studies large sample asymptotic properties of the Maximum Likelihood Estimator (MLE) for the parameter of a continuous time Markov chain, observed in white noise. Using the method of weak convergence of likelihoods due to I.Ibragimov and R.Khasminskii [14], consistency, asymptotic normality and convergence of moments are established for MLE under certain strong ergodicity conditions o...

We consider basic ergodicity properties of adaptive MCMC algorithms under minimal assumptions, using coupling constructions. We prove convergence in distribution and a weak law of large numbers. We also give counter-examples to demonstrate that the assumptions we make are not redundant.