AbstractConsider the class of discrete time, general state space Markov chains which satisfy a “uniform ergodicity under sampling” condition. There are many ways to quantify the notion of “mixing time”, i.e., time to approach stationarity from a worst initial state. We prove results asserting equivalence (up to universal constants) of different quantifications of mixing time. This work combines three areas of Markov theory which are rarely connected: the potential-theoretical characterization of optimal stopping times, the theory of stability and convergence to stationarity for general-state chains, and the theory surrounding mixing times for finite-state chains
Given an irreducible discrete time Markov chain on a finite state space, we consider the largest exp...
We determine the mixing time (up to a constant factor) of the Markov chain whose state space consist...
This book is an introduction to the modern approach to the theory of Markov chains. The main goal of...
AbstractA measure of the “mixing time” or “time to stationarity” in a finite irreducible discrete ti...
The focus of the thesis is the convergence of irreducible aperiodic homoge- neous Markov chains with...
In the past few years we have seen a surge in the theory of finite Markov chains, by way of new tech...
The distribution of the “mixing time” or the “time to stationarity” in a discrete time irreducible M...
A classic result in the theory of Markov Chains is that irreducible and aperiodic chains converge to...
In this thesis, we deal with the upper and lower bounds for the mixing time of reversi- ble homogene...
AbstractIn an earlier paper [J.J. Hunter, Mixing times with applications to perturbed Markov chains,...
The derivation of the expected time to coupling in a Markov chain and its relation to the expected t...
The aim of this thesis is to present several (co-authored) works of the author concerning applicatio...
This article provides the first procedure for computing a fully data-dependent interval that traps t...
We consider irreducible Markov chains on a finite state space. We show that the mixing time of any s...
AbstractThe derivation of the expected time to coupling in a Markov chain and its relation to the ex...
Given an irreducible discrete time Markov chain on a finite state space, we consider the largest exp...
We determine the mixing time (up to a constant factor) of the Markov chain whose state space consist...
This book is an introduction to the modern approach to the theory of Markov chains. The main goal of...
AbstractA measure of the “mixing time” or “time to stationarity” in a finite irreducible discrete ti...
The focus of the thesis is the convergence of irreducible aperiodic homoge- neous Markov chains with...
In the past few years we have seen a surge in the theory of finite Markov chains, by way of new tech...
The distribution of the “mixing time” or the “time to stationarity” in a discrete time irreducible M...
A classic result in the theory of Markov Chains is that irreducible and aperiodic chains converge to...
In this thesis, we deal with the upper and lower bounds for the mixing time of reversi- ble homogene...
AbstractIn an earlier paper [J.J. Hunter, Mixing times with applications to perturbed Markov chains,...
The derivation of the expected time to coupling in a Markov chain and its relation to the expected t...
The aim of this thesis is to present several (co-authored) works of the author concerning applicatio...
This article provides the first procedure for computing a fully data-dependent interval that traps t...
We consider irreducible Markov chains on a finite state space. We show that the mixing time of any s...
AbstractThe derivation of the expected time to coupling in a Markov chain and its relation to the ex...
Given an irreducible discrete time Markov chain on a finite state space, we consider the largest exp...
We determine the mixing time (up to a constant factor) of the Markov chain whose state space consist...
This book is an introduction to the modern approach to the theory of Markov chains. The main goal of...