منابع مشابه
Markov Chain Monte Carlo
Markov chain Monte Carlo is an umbrella term for algorithms that use Markov chains to sample from a given probability distribution. This paper is a brief examination of Markov chain Monte Carlo and its usage. We begin by discussing Markov chains and the ergodicity, convergence, and reversibility thereof before proceeding to a short overview of Markov chain Monte Carlo and the use of mixing time...
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This paper gives a brief introduction to Markov Chain Monte Carlo methods, which offer a general framework for calculating difficult integrals. We start with the basic theory of Markov chains and build up to a theorem that characterizes convergent chains. We then discuss the MetropolisHastings algorithm.
متن کاملMarkov chain Monte Carlo
One of the simplest and most powerful practical uses of the ergodic theory of Markov chains is in Markov chain Monte Carlo (MCMC). Suppose we wish to simulate from a probability density π (which will be called the target density) but that direct simulation is either impossible or practically infeasible (possibly due to the high dimensionality of π). This generic problem occurs in diverse scient...
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Regeneration is a useful tool in Markov chain Monte Carlo simulation, since it can be used to side-step the burn-in problem and to construct estimates of the variance of parameter estimates themselves. Unfortunately, it is often diÆcult to take advantage of, since for most chains, no recurrent atom exists, and it is not always easy to use Nummelin's splitting method to identify regeneration poi...
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Decision and optimization problems involving graphs arise in many areas of artificial intelligence, including probabilistic networks, robot navigation, and network design. Many such problems are NP-complete; this has necessitated the development of approximation methods, most of which are very complex and highly problemspecific. We propose a straightforward, practical approach to algorithm desi...
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ژورنال
عنوان ژورنال: Statistical Science
سال: 1992
ISSN: 0883-4237
DOI: 10.1214/ss/1177011137