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Coupling from the past

Among Markov chain Monte Carlo (MCMC) algorithms, coupling from the past is a method for sampling from the stationary distribution of a Markov chain… 
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Papers overview

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2019
2019
We give an algorithm for perfect sampling from the uniform distribution on proper $k$-colorings of graphs of maximum degree… 
Review
2017
Review
2017
We aim to explore Coupling from the Past (CFTP), an algorithm designed to obtain a perfect sampling from the stationary… 
Review
2016
Review
2016
Markov Chain Monte Carlo (MCMC) methods are a class of algorithms for sampling from a desired probability distribution. While… 
2014
2014
The goal of a Markov Chain Monte Carlo (MCMC) simulation is to generate samples from a target probability distribution π by… 
2011
2011
Sometimes one wants a sample from an unknown distribution. We call a realization, without any errors, of a random variable with… 
2011
2011
We discuss the Coupling from the Past Algorithm within the context of synchronising words and reformulate it. Our findings are… 
2008
2008
This paper proposed a new algorithm of Coupling from the Past (CFTP) with directional threshold. CFTP, also called Exact Sampling… 
2006
2006
We saw in the last lecture how Markov chains can be useful algorithmically. If we have a probability distribution we’d like to… 
Review
1998
Review
1998
There are now methods for organising a Markov chain Monte Carlo simulation so that it can be guaranteed that the state of the… 
1998
1998
Propp and Wilson (1996,1998) described a protocol called coupling from the past (CFTP) for exact sampling from the steady-state…