Contemplating Evidence : properties , extensions of , and alternatives to Nested Sampling

  title={Contemplating Evidence : properties , extensions of , and alternatives to Nested Sampling},
  author={Christine Obert},
Nested sampling is a novel simulation method for approximating marginal likelihoods, proposed by Skilling (2007a,b). We establish that nested sampling leads to an error that vanishes at the standard Monte Carlo rate N−1/2, where N is a tuning parameter that is proportional to the computational effort, and that this error is asymptotically Gaussian. We show that the corresponding asymptotic variance typically grows linearly with the dimension of the parameter. We use these results to discuss the… CONTINUE READING
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Referenced Papers

Publications referenced by this paper.
Showing 1-10 of 33 references

Reversible jump MCMC computation and Bayesian model determination

  • P. Green
  • Biometrika, 82(4):711–732.
  • 1995
Highly Influential
4 Excerpts

Comments on ‘ Nested Sampling ’ by John Skilling

  • Bayesian Statistics
  • 2007

Data Analysis Using Regression and Multilevel/Hierarchical Models

  • A. Gelman, J. Hill
  • Cambridge University Press.
  • 2006
1 Excerpt

Efficient Bayes factor estimation from the reversible jump output

  • Bartolucci, L. F. Scaccia, A. Mira
  • Biometrika, 93:41–52.
  • 2006

Estimating marginal likelihoods for mixture and Markov switching models using bridge sampling techniques

  • J. Bernardo, M. Bayarri, +5 authors S. RÜHWIRTH-S CHNATTER
  • The Econometrics Journal
  • 2004

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