We analyze the speed of convergence to stationarity for a specific stochastic system consisting of a finite number of interacting particles on the circle. We define a coupling and a martingale related to this coupling to show that the time needed to approach stationarity is a polynomial in the number of particles of degree at most 12, and thus prove that the chain is rapidly mixing. This is partly due to the fact that the coupling time happens before the martingale escapes from a certain strip. We use a relaxation time related to Poincaré's characterization of the second largest eigenvalue of the chain, to lower bound the time to stationarity by a polynomial of degree 3.Continuous state space Markov chain Rapidly mixing Interacting particle...
The derivation of the expected time to coupling in a Markov chain and its relation to the expected t...
We present a new technique for constructing and analyzing couplings to bound the convergence rate of...
For the probabilistic model of shuffling by random transpositions we provide a coupling construction...
AbstractWe analyze the speed of convergence to stationarity for a specific stochastic system consist...
In the present work we study two methods for estimating the rate of convergence of marginal distribu...
Graduation date: 2018Markov chains have long been used to sample from probability distributions and ...
In this thesis we discuss concentration inequalities, relaxation to equilibrium of stochastic dynami...
Algorithms are introduced that produce optimal Markovian couplings for large finite-state-space disc...
In the past few years we have seen a surge in the theory of finite Markov chains, by way of new tech...
We determine the mixing time (up to a constant factor) of the Markov chain whose state space consist...
Convergence of the marginal distribution of a Markov chain to its stationary distribution is an esse...
AbstractMixing time quantifies the convergence speed of a Markov chain to the stationary distributio...
This book is an introduction to the modern approach to the theory of Markov chains. The main goal of...
Abstract. We prove that Broder’s Markov chain for approximate sampling near-perfect and perfect matc...
We show that no Markovian coupling argument can prove rapid mixing of the Jerrum-Sinclair Markov cha...
The derivation of the expected time to coupling in a Markov chain and its relation to the expected t...
We present a new technique for constructing and analyzing couplings to bound the convergence rate of...
For the probabilistic model of shuffling by random transpositions we provide a coupling construction...
AbstractWe analyze the speed of convergence to stationarity for a specific stochastic system consist...
In the present work we study two methods for estimating the rate of convergence of marginal distribu...
Graduation date: 2018Markov chains have long been used to sample from probability distributions and ...
In this thesis we discuss concentration inequalities, relaxation to equilibrium of stochastic dynami...
Algorithms are introduced that produce optimal Markovian couplings for large finite-state-space disc...
In the past few years we have seen a surge in the theory of finite Markov chains, by way of new tech...
We determine the mixing time (up to a constant factor) of the Markov chain whose state space consist...
Convergence of the marginal distribution of a Markov chain to its stationary distribution is an esse...
AbstractMixing time quantifies the convergence speed of a Markov chain to the stationary distributio...
This book is an introduction to the modern approach to the theory of Markov chains. The main goal of...
Abstract. We prove that Broder’s Markov chain for approximate sampling near-perfect and perfect matc...
We show that no Markovian coupling argument can prove rapid mixing of the Jerrum-Sinclair Markov cha...
The derivation of the expected time to coupling in a Markov chain and its relation to the expected t...
We present a new technique for constructing and analyzing couplings to bound the convergence rate of...
For the probabilistic model of shuffling by random transpositions we provide a coupling construction...