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搜索结果: 1-15 共查到管理学 Markov Chain Monte Carlo相关记录17条 . 查询时间(0.078 秒)
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...
We present a new way of converting a reversible finite Markov chain into a non-reversible one, with a theoretical guarantee that the asymptotic variance of the MCMC estimator based on the non-reversib...
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...
Auxiliary variable methods such as the Parallel Tempering and the cluster Monte Carlo methods generate samples that follow a target distri-bution by using proposal and auxiliary distributions.In sampl...
Let $\mathscr{P}(E)$ be the space of probability measures on a measurable space $(E,\mathcal{E})$. In this paper we introduce a class of nonlinear Markov chain Monte Carlo (MCMC) methods for simulatin...
In this article we propose a novel MCMC method based on deterministic transformations T : X x D --> X where X is the state-space and D is some set which may or may not be a subset of X. We refer to ou...
We develop exact Markov chain Monte Carlo methods for discretely-sampled, directly and indirectly observed diffusions. The qualification "exact" refers to the fact that the invariant and limiting dist...
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 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...
Hidden Markov models (HMMs) and related models have become stan- dard in statistics during the last 15C2 years, with applications in diverse areas like speech and other statistical signal processing...

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