Multivariate Output Analysis for Markov chain Monte Carlo

@article{Vats2015MultivariateOA,
  title={Multivariate Output Analysis for Markov chain Monte Carlo},
  author={Dootika Vats and James M. Flegal and G. Jones},
  journal={arXiv: Statistics Theory},
  year={2015}
}
  • Dootika Vats, James M. Flegal, G. Jones
  • Published 2015
  • Mathematics
  • arXiv: Statistics Theory
  • Markov chain Monte Carlo (MCMC) produces a correlated sample for estimating expectations with respect to a target distribution. A fundamental question is when should sampling stop so that we have good estimates of the desired quantities? The key to answering this question lies in assessing the Monte Carlo error through a multivariate Markov chain central limit theorem (CLT). The multivariate nature of this Monte Carlo error largely has been ignored in the MCMC literature. We present a… CONTINUE READING

    Figures and Tables from this paper.

    Substrate effects on the creep properties of pure Sn solder joints
    2
    Macrophagic Myofaciitis a Vaccine (alum) Autoimmune-Related Disease
    60
    Visualizing Monte Carlo Error and Terminating Markov Chain Monte Carlo Simulation
    Estimating Monte Carlo variance from multiple Markov chains
    Revisiting the Gelman-Rubin Diagnostic
    18
    Weighted batch means estimators in Markov chain Monte Carlo.
    13

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 72 REFERENCES
    Six cases of reliability study of Pb-free solder joints in electronic packaging technology
    1218
    Combating VR sickness through subtle dynamic field-of-view modification
    187
    MCA has more to say.
    90
    Cooperative spectrum sensing in cognitive radio networks: A survey
    1590
    CFTR regulates phagosome acidification in macrophages and alters bactericidal activity
    383
    Prodrugs—from Serendipity to Rational Design
    328
    Phys
    • 1995