Add to ORKGIn this thesis we study the estimation of speed of convergence of Markov chains to their stacionary distributions. For that purpose we will use the method of strong stationary times. We focus on irreducible and aperiodic chains only since in that case the existence of exactly one stationary distribution is guaranteed. We introduce the mixing time for a Markov chain as the time needed for the marginal distribution of the chain to be sufficiently close to the stationary dis- tribution. The distance between two distributions is measured by the total variation distance. The main goal of this thesis is to construct an appropriate strong stationary time for selected chains and then use it for obtaining an upper bound for the mixing time
AbstractFor finite Markov chains the eigenvalues of P can be used to characterize the chain and also...
The random mapping construction of strong stationary times is applied here to finite Heisenberg rand...
In the past few years we have seen a surge in the theory of finite Markov chains, by way of new tech...
In this thesis we study the estimation of speed of convergence of Markov chains to their stacionary ...
In the present work we study two methods for estimating the rate of convergence of marginal distribu...
A classic result in the theory of Markov Chains is that irreducible and aperiodic chains converge to...
This book is an introduction to the modern approach to the theory of Markov chains. The main goal of...
The focus of the thesis is the convergence of irreducible aperiodic homoge- neous Markov chains with...
International audienceA necessary and sufficient condition is obtained for the existence of strong s...
AbstractThere are several techniques for obtaining bounds on the rate of convergence to the stationa...
AbstractA coupling method is used to obtain the explicit upper and lower bounds for convergence rate...
Graduation date: 2018Markov chains have long been used to sample from probability distributions and ...
In this paper, we investigate computable lower bounds for the best strongly ergodic rate of converg...
Convergence of the marginal distribution of a Markov chain to its stationary distribution is an esse...
In this thesis, we deal with the upper and lower bounds for the mixing time of reversi- ble homogene...
AbstractFor finite Markov chains the eigenvalues of P can be used to characterize the chain and also...
The random mapping construction of strong stationary times is applied here to finite Heisenberg rand...
In the past few years we have seen a surge in the theory of finite Markov chains, by way of new tech...
In this thesis we study the estimation of speed of convergence of Markov chains to their stacionary ...
In the present work we study two methods for estimating the rate of convergence of marginal distribu...
A classic result in the theory of Markov Chains is that irreducible and aperiodic chains converge to...
This book is an introduction to the modern approach to the theory of Markov chains. The main goal of...
The focus of the thesis is the convergence of irreducible aperiodic homoge- neous Markov chains with...
International audienceA necessary and sufficient condition is obtained for the existence of strong s...
AbstractThere are several techniques for obtaining bounds on the rate of convergence to the stationa...
AbstractA coupling method is used to obtain the explicit upper and lower bounds for convergence rate...
Graduation date: 2018Markov chains have long been used to sample from probability distributions and ...
In this paper, we investigate computable lower bounds for the best strongly ergodic rate of converg...
Convergence of the marginal distribution of a Markov chain to its stationary distribution is an esse...
In this thesis, we deal with the upper and lower bounds for the mixing time of reversi- ble homogene...
AbstractFor finite Markov chains the eigenvalues of P can be used to characterize the chain and also...
The random mapping construction of strong stationary times is applied here to finite Heisenberg rand...
In the past few years we have seen a surge in the theory of finite Markov chains, by way of new tech...