In this paper, we consider the question of which convergence properties of Markov chains are preserved under small perturbations. Properties considered include geometric ergodicity and rates of convergence. Perturbations considered include roundoff error from computer simulation. We are motivated primarily by interest in Markov chain Monte Carlo algorithms
International audienceIn this paper, we establish explicit convergence rates for Markov chains in Wa...
International audienceIn this paper, we establish explicit convergence rates for Markov chains in Wa...
Summary. Bounds on convergence rates for Markov chains are a very widely-studied topic, motivated la...
UnrestrictedSince we have the preliminary fact that the irreducible, aperiodic and reversible Markov...
This paper surveys various results about Markov chains on general (non-countable) state spaces. It b...
communicated by I. Pinelis Abstract. For the distribution of a finite, homogeneous, continuous-time ...
In the thesis, we study ergodicity of adaptive Markov Chain Monte Carlo methods (MCMC) based on two ...
. We present a general method for proving rigorous, a priori bounds on the number of iterations requ...
To sample from distributions in high dimensional spaces or finite large sets di-rectly is not feasib...
We give computable bounds on the rate of convergence of the transition probabil-ities to the station...
The Monte Carlo within Metropolis (MCwM) algorithm, interpreted as a perturbed Metropolis–Hastings (...
We give necessary and sufficient conditions for geometric and polynomial ergodicity of a Markov chai...
International audienceIn this paper, we establish explicit convergence rates for Markov chains in Wa...
International audienceIn this paper, we establish explicit convergence rates for Markov chains in Wa...
We argue that Monte Carlo algorithms are ideally suited to parallel computing, and that “parallel Mo...
International audienceIn this paper, we establish explicit convergence rates for Markov chains in Wa...
International audienceIn this paper, we establish explicit convergence rates for Markov chains in Wa...
Summary. Bounds on convergence rates for Markov chains are a very widely-studied topic, motivated la...
UnrestrictedSince we have the preliminary fact that the irreducible, aperiodic and reversible Markov...
This paper surveys various results about Markov chains on general (non-countable) state spaces. It b...
communicated by I. Pinelis Abstract. For the distribution of a finite, homogeneous, continuous-time ...
In the thesis, we study ergodicity of adaptive Markov Chain Monte Carlo methods (MCMC) based on two ...
. We present a general method for proving rigorous, a priori bounds on the number of iterations requ...
To sample from distributions in high dimensional spaces or finite large sets di-rectly is not feasib...
We give computable bounds on the rate of convergence of the transition probabil-ities to the station...
The Monte Carlo within Metropolis (MCwM) algorithm, interpreted as a perturbed Metropolis–Hastings (...
We give necessary and sufficient conditions for geometric and polynomial ergodicity of a Markov chai...
International audienceIn this paper, we establish explicit convergence rates for Markov chains in Wa...
International audienceIn this paper, we establish explicit convergence rates for Markov chains in Wa...
We argue that Monte Carlo algorithms are ideally suited to parallel computing, and that “parallel Mo...
International audienceIn this paper, we establish explicit convergence rates for Markov chains in Wa...
International audienceIn this paper, we establish explicit convergence rates for Markov chains in Wa...
Summary. Bounds on convergence rates for Markov chains are a very widely-studied topic, motivated la...
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