Asymptotically Independent Samplers

  • Luca Martino
  • David Luengo
  • Joaquín Míguez
Part of the Statistics and Computing book series (SCO)


Markov Chain Monte Carlo (MCMC) methods are possibly the most popular tools for random sampling nowadays. They generate “chains” (sequences) of samples from a target distribution that can be selected with few constraints. However, as highlighted by the term “chain,” the draws output by the MCMC method are statistically dependent (and often highly correlated), which makes such algorithms not directly comparable with the methods in the rest of this monograph. In this chapter, we describe two families of non-standard MCMC techniques that enjoy the property of producing samples that become asymptotically independent as a parameter grows to infinity or the number of random draws in the algorithm is increased. The methods of the first family are based on generating a pool of candidate samples at each iteration of the chain, instead of only one as in conventional procedures. The techniques in the second family rely on an adaptive, non-parametric approximation of the target density, which is improved as new samples are generated. We describe the general methodology for the two families, and provide some specific algorithms as examples.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Luca Martino
    • 1
  • David Luengo
    • 2
  • Joaquín Míguez
    • 1
  1. 1.Department of Signal Theory and CommunicationsCarlos III University of MadridMadridSpain
  2. 2.Department of Signal Theory and CommunicationsTechnical University of MadridMadridSpain

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