AbstractIn this paper we introduce for the first time the concept of a perturbed nonhomogeneous Markov system (P-NHMS). The expected population structure is found and its asymptotic behavior is provided under more realistic assumptions than previous studies, by relaxing the assumption that the imbedded nonhomogeneous Markov chain of a NHMS is converging to a homogeneous Markov chain. Also we study the sensitivity of the limiting expected structure on perturbations of the limiting input probabilities for a NHMS. Moreover, the asymptotic behavior of the variances and covariances of the class sizes of a P-NHMS is considered and found in an elegant closed analytic form
AbstractThe purpose of this article is to present results concerning the sensitivity of the stationa...
AbstractUsing the maximum likelihood principle, nonparametric estimators are derived for discrete ti...
AbstractLet A1, A2,…, be commuting intensity matrices of homogeneous, continuous-time Markov chains....
AbstractIn this paper we introduce for the first time the concept of a perturbed nonhomogeneous Mark...
AbstractWe study the asymptotic behavior of a nonhomogeneous semi-Markov system (population) in disc...
AbstractWe find the sets of d-periodic asymptotically attainable structures, and we establish the pe...
Nearly uncoupled Markov chains (aka nearly completely decomposable Markov chains) arise in a variety...
AbstractThe purpose of this paper is to review and compare the existing perturbation bounds for the ...
AbstractO'Cinneide presented an entrywise perturbation theorem for Markov chains. The error bound he...
summary:Let $p_t$ be a vector of absolute distributions of probabilities in an irreducible aperiodic...
Due to the character of the original source materials and the nature of batch digitization, quality ...
The attached file may be somewhat different from the published versionInternational audienceIn this ...
This paper considers maximum likelihood (ML) estimation in a large class of models with hidden Marko...
AbstractThe sensitivity of the unique stationary distribution of a finite Markov chain which has a s...
AbstractA classical result of Markov chain theory states that if A is primitive and stochastic then ...
AbstractThe purpose of this article is to present results concerning the sensitivity of the stationa...
AbstractUsing the maximum likelihood principle, nonparametric estimators are derived for discrete ti...
AbstractLet A1, A2,…, be commuting intensity matrices of homogeneous, continuous-time Markov chains....
AbstractIn this paper we introduce for the first time the concept of a perturbed nonhomogeneous Mark...
AbstractWe study the asymptotic behavior of a nonhomogeneous semi-Markov system (population) in disc...
AbstractWe find the sets of d-periodic asymptotically attainable structures, and we establish the pe...
Nearly uncoupled Markov chains (aka nearly completely decomposable Markov chains) arise in a variety...
AbstractThe purpose of this paper is to review and compare the existing perturbation bounds for the ...
AbstractO'Cinneide presented an entrywise perturbation theorem for Markov chains. The error bound he...
summary:Let $p_t$ be a vector of absolute distributions of probabilities in an irreducible aperiodic...
Due to the character of the original source materials and the nature of batch digitization, quality ...
The attached file may be somewhat different from the published versionInternational audienceIn this ...
This paper considers maximum likelihood (ML) estimation in a large class of models with hidden Marko...
AbstractThe sensitivity of the unique stationary distribution of a finite Markov chain which has a s...
AbstractA classical result of Markov chain theory states that if A is primitive and stochastic then ...
AbstractThe purpose of this article is to present results concerning the sensitivity of the stationa...
AbstractUsing the maximum likelihood principle, nonparametric estimators are derived for discrete ti...
AbstractLet A1, A2,…, be commuting intensity matrices of homogeneous, continuous-time Markov chains....