In this paper, we study the problem of sampling (exactly) uniformly from the set of linear extensions of an arbitrary partial order. Previous Markov chain techniques have yielded algorithms that generate approximately uniform samples. Here, we create a bounding chain for one such Markov chain, and by using a non-Markovian coupling together with a modified form of coupling from the past, we build an algorithm for perfectly generating samples. The expected running time of the procedure is O(n 3 ln n), making the technique as fast as the mixing time of the Karzanov/Khachiyan chain upon which it is based
Perfect sampling allows exact simulation of random variables from the stationary measure of a Markov...
Sampling of combinatorial structures is an important statistical tool used in applications in a numb...
We propose and analyze two new MCMC sampling algorithms, the Vaidya walk and the John walk, for gene...
AbstractIn this paper, we study the problem of sampling (exactly) uniformly from the set of linear e...
AbstractIn this paper, we study the problem of sampling (exactly) uniformly from the set of linear e...
AbstractThis paper examines the problem of sampling (almost) uniformly from the set of linear extens...
AbstractThis paper examines the problem of sampling (almost) uniformly from the set of linear extens...
Perfect sampling is a technique that uses coupling arguments to provide a sample from the stationary...
Abstract. We study the generation of uniformly distributed linear extensions using Markov chains. In...
International audiencePerfect sampling is a technique that uses coupling arguments to provide a samp...
International audiencePerfect sampling is a technique that uses coupling arguments to provide a samp...
Perfect sampling, or coupling from the past enables one to compute unbiased samples of the stationar...
International audienceIn this paper, we introduce a uniform random sampler for linear extensions of ...
For a large class of examples arising in statistical physics known as attractive spin systems (e.g.,...
Markov Chain Monte Carlo algorithms are often used to sample combinatorial structures such as matchi...
Perfect sampling allows exact simulation of random variables from the stationary measure of a Markov...
Sampling of combinatorial structures is an important statistical tool used in applications in a numb...
We propose and analyze two new MCMC sampling algorithms, the Vaidya walk and the John walk, for gene...
AbstractIn this paper, we study the problem of sampling (exactly) uniformly from the set of linear e...
AbstractIn this paper, we study the problem of sampling (exactly) uniformly from the set of linear e...
AbstractThis paper examines the problem of sampling (almost) uniformly from the set of linear extens...
AbstractThis paper examines the problem of sampling (almost) uniformly from the set of linear extens...
Perfect sampling is a technique that uses coupling arguments to provide a sample from the stationary...
Abstract. We study the generation of uniformly distributed linear extensions using Markov chains. In...
International audiencePerfect sampling is a technique that uses coupling arguments to provide a samp...
International audiencePerfect sampling is a technique that uses coupling arguments to provide a samp...
Perfect sampling, or coupling from the past enables one to compute unbiased samples of the stationar...
International audienceIn this paper, we introduce a uniform random sampler for linear extensions of ...
For a large class of examples arising in statistical physics known as attractive spin systems (e.g.,...
Markov Chain Monte Carlo algorithms are often used to sample combinatorial structures such as matchi...
Perfect sampling allows exact simulation of random variables from the stationary measure of a Markov...
Sampling of combinatorial structures is an important statistical tool used in applications in a numb...
We propose and analyze two new MCMC sampling algorithms, the Vaidya walk and the John walk, for gene...