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We propose an efficient Markov Chain Monte Carlo method for sampling equilibrium distributions for stochastic lattice models, capable of handling correctly long and short-range particle interactions. ...
We propose a new algorithm to do posterior sampling of Kingman's coalescent, based upon the Particle Markov Chain Monte Carlo methodology. Specifically, the algorithm is an instantiation of the Partic...
In [Chen, D., Owen, Ann. Stat., 39, 673--701, 2011] Markov chain Monte Carlo (MCMC) was studied under the assumption that the driver sequence is a deterministic sequence rather than independent U(0,1)...
In Adaptive Markov Chain Monte Carlo (AMCMC) simulation, classical estimators of asymptotic variances are inconsistent in general. In this work we establish that despite this inconsistency, confidence...
We introduce a new geometric approach that constructs a transition kernel of Markov chain. Our method always minimizes the average rejection rate and even reduce it to zero in many relevant cases, whi...
The random numbers drivingMarkov chainMonte Carlo (MCMC) simulation are usually modeled as independent U(0, 1) random variables. Tribble [Markov chain Monte Carlo algorithms using completely unifor...
We present a framework for obtaining explicit bounds on the rate of convergence to equilibrium of a Markov chain on a general state space, with respect to both total variation and Wasserstein distance...
A general purpose variance reduction technique for Markov chain Monte Carlo estimators based on the zero-variance principle introduced in the physics literature by Assaraf and Caffarel (1999, 2003), i...
In this paper, we introduce the notion of efficiency (consistency) and examine some asymptotic properties of Markov chain Monte Carlo methods. We apply these results to the Gibbs sampler for independe...
The correspondence between the cross-entropymethod and the zero-variance approximation to simulate a rare event problem in Markov chains is shown. This leads to a sufficient condition that the cross...
The reversible jump Markov chain Monte Carlo sampler (Green, 1995) provides a general framework for Markov chain Monte Carlo (MCMC) simulation in which the dimension of the parameter space can vary ...
In Bayesian inference, the posterior distribution for parameters  2  is given by (jy) / (yj)(), where one's prior beliefs about the unknown parameters, as expressed through the prior distrib...
In this note we attempt to trace the history and development of Markov chain Monte Carlo (MCMC) from its early inception in the late 1940’s through its use today. We see how the earlier stages of th...
In this paper,we combine useful aspects of both approaches.On the one hand,we are inspired by the discretization, where filtering for the state process is possible,on the other hand,we catch attracti...

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