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BibTeX entry
@Article{Tierney:1994:MCE,
author = "Luke Tierney",
title = "{Markov} Chains for Exploring Posterior
Distributions",
journal = j-ANN-STAT,
volume = "22",
number = "4",
pages = "1701--1728",
month = dec,
year = "1994",
CODEN = "ASTSC7",
DOI = "https://doi.org/10.1214/aos/1176325750",
ISSN = "0090-5364 (print), 2168-8966 (electronic)",
ISSN-L = "0090-5364",
bibdate = "Wed Jun 4 06:40:27 MDT 2014",
bibsource = "http://www.math.utah.edu/pub/bibnet/authors/m/metropolis-nicholas.bib;
http://www.math.utah.edu/pub/bibnet/authors/t/teller-edward.bib;
http://www.math.utah.edu/pub/tex/bib/annstat1990.bib;
http://www.math.utah.edu/pub/tex/bib/prng.bib",
URL = "http://projecteuclid.org/euclid.aos/1176325750;
http://www.jstor.org/stable/2242477",
abstract = "Several Markov chain methods are available for
sampling from a posterior distribution. Two important
examples are the Gibbs sampler and the Metropolis
algorithm. In addition, several strategies are
available for constructing hybrid algorithms. This
paper outlines some of the basic methods and strategies
and discusses some related theoretical and practical
issues. On the theoretical side, results from the
theory of general state space Markov chains can be used
to obtain convergence rates, laws of large numbers and
central limit theorems for estimates obtained from
Markov chain methods. These theoretical results can be
used to guide the construction of more efficient
algorithms. For the practical use of Markov chain
methods, standard simulation methodology provides
several variance reduction techniques and also give
guidance on the choice of sample size and allocation.",
acknowledgement = ack-nhfb,
fjournal = "Annals of Statistics",
journal-URL = "http://projecteuclid.org/all/euclid.aos/",
remark = "According to \cite{Hitchcock:2003:HMH}, this paper is
the origin of the MCMC (Markov Chain Monte Carlo)
method.",
}
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