[Practical Markov Chain Monte Carlo]: Rejoinder: Replication without Contrition
نویسندگان
چکیده
منابع مشابه
Markov chain Monte Carlo without likelihoods.
Many stochastic simulation approaches for generating observations from a posterior distribution depend on knowing a likelihood function. However, for many complex probability models, such likelihoods are either impossible or computationally prohibitive to obtain. Here we present a Markov chain Monte Carlo method for generating observations from a posterior distribution without the use of likeli...
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1. Dirichlet process priors. Ishwaran has presented an analysis of a model (the Rasch model) using MCMC with Dirichlet process priors, following the approach of Escobar (1994) and MacEachern (1994). The analysis is a good example of how applied statisticians should approach such problems. Ishwaran is well informed about available theoretical results, and makes use of them where possible. At the...
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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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ژورنال
عنوان ژورنال: Statistical Science
سال: 1992
ISSN: 0883-4237
DOI: 10.1214/ss/1177011148