Adaptive Gibbs samplers and related MCMC methods

@article{Latuszynski2013AdaptiveGS,
  title={Adaptive Gibbs samplers and related MCMC methods},
  author={K. Latuszynski and G. Roberts and J. Rosenthal},
  journal={Annals of Applied Probability},
  year={2013},
  volume={23},
  pages={66-98}
}
  • K. Latuszynski, G. Roberts, J. Rosenthal
  • Published 2013
  • Mathematics
  • Annals of Applied Probability
  • We consider various versions of adaptive Gibbs and Metropolis- within-Gibbs samplers, which update their selection probabilities (and per- haps also their proposal distributions) on the y during a run, by learning as they go in an attempt to optimise the algorithm. We present a cautionary example of how even a simple-seeming adaptive Gibbs sampler may fail to converge. We then present various positive results guaranteeing convergence of adaptive Gibbs samplers under certain conditions. AMS 2000… CONTINUE READING
    Visualizing ontologies with VOWL
    119
    Composition and applications of focus libraries to phenotypic assays
    24
    Applying Exergaming Input to Standard Commercial Digital Games
    1

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 69 REFERENCES
    Phys, Rev. Lett
    • 1989
    Overview of grammar acquisition research
    4
    Multi-objective robust optimization using a sensitivity region concept
    178
    Thalamic and cortical processing in rat models of clinical pain
    • 1995
    I and J
    117789
    Mining Very Large Datasets with Support Vector Machine Algorithms
    21