منابع مشابه
Explaining the Perfect Sampler
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متن کاملExplaining the Gibbs Sampler
Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your perso...
متن کاملExplaining Guides Learners Towards Perfect Patterns, Not Perfect Prediction
When learners explain to themselves as they encounter new information, they recruit a suite of processes that influence subsequent learning. One consequence is that learners are more likely to discover exceptionless rules that underlie what they are trying to explain. Here we investigate what it is about exceptionless rules that satisfies the demands of explanation. Are exceptions unwelcome bec...
متن کاملThe Generalized Gibbs Sampler and the Neighborhood Sampler
The Generalized Gibbs Sampler (GGS) is a recently proposed Markov chain Monte Carlo (MCMC) technique that is particularly useful for sampling from distributions defined on spaces in which the dimension varies from point to point or in which points are not easily defined in terms of co-ordinates. Such spaces arise in problems involving model selection and model averaging and in a number of inter...
متن کاملRandomized approximation scheme and perfect sampler for closed Jackson networks with multiple servers
In this paper, we propose a fully polynomial-time randomized approximation scheme (FPRAS) for the closed Jackson network. Our algorithm is based on Markov chain Monte Carlo (MCMC) method. Thus, our scheme returns an approximate solution, of which the size of error satisfies a given error rate. To our knowledge, the algorithm is the first polynomial time MCMC algorithm for closed Jackson network...
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ژورنال
عنوان ژورنال: The American Statistician
سال: 2001
ISSN: 0003-1305,1537-2731
DOI: 10.1198/000313001753272240