Quasi-Reliable Estimates of Effective Sample Size

@article{Fang2017QuasiReliableEO,
  title={Quasi-Reliable Estimates of Effective Sample Size},
  author={Youhan Fang and Yudong Cao and R. Skeel},
  journal={arXiv: Computation},
  year={2017}
}
The efficiency of a Markov chain Monte Carlo algorithm might be measured by the cost of generating one independent sample, or equivalently, the total cost divided by the effective sample size, defined in terms of the integrated autocorrelation time. To ensure the reliability of such an estimate, it is suggested that there be an adequate sampling of state space--- to the extent that this can be determined from the available samples. A possible method for doing this is derived and evaluated. 

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References

SHOWING 1-10 OF 15 REFERENCES
Practical Markov Chain Monte Carlo
Rational Construction of Stochastic Numerical Methods for Molecular Sampling
Postprocessed Integrators for the High Order Integration of Ergodic SDEs
  • G. Vilmart
  • Mathematics, Computer Science
  • SIAM J. Sci. Comput.
  • 2015
A generalized guided Monte Carlo algorithm
The computation of averages from equilibrium and nonequilibrium Langevin molecular dynamics
Hybrid Monte Carlo
Monte Carlo Methods in Statistical Mechanics: Foundations and New Algorithms
Exponential convergence of Langevin distributions and their discrete approximations
Bayesian Data Analysis
The Fokker-Planck Equation
...
1
2
...