Entry Tierney:1994:MCE from annstat1990.bib

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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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