Respondent-driven sampling as Markov chain Monte Carlo
نویسندگان
چکیده
منابع مشابه
Respondent-driven sampling as Markov chain Monte Carlo.
Respondent-driven sampling (RDS) is a recently introduced, and now widely used, technique for estimating disease prevalence in hidden populations. RDS data are collected through a snowball mechanism, in which current sample members recruit future sample members. In this paper we present RDS as Markov chain Monte Carlo importance sampling, and we examine the effects of community structure and th...
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A major limitation towards more widespread implementation of Bayesian approaches is that obtaining the posterior distribution often requires the integration of high-dimensional functions. This can be computationally very difficult, but several approaches short of direct integration have been proposed (reviewed by Smith 1991, Evans and Swartz 1995, Tanner 1996). We focus here on Markov Chain Mon...
متن کاملMarkov Chain Monte Carlo and Gibbs Sampling
A major limitation towards more widespread implementation of Bayesian approaches is that obtaining the posterior distribution often requires the integration of high-dimensional functions. This can be computationally very difficult, but several approaches short of direct integration have been proposed (reviewed by Smith 1991, Evans and Swartz 1995, Tanner 1996). We focus here on Markov Chain Mon...
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ژورنال
عنوان ژورنال: Statistics in Medicine
سال: 2009
ISSN: 0277-6715
DOI: 10.1002/sim.3613