Approximate Bayesian Computation : a non-parametric perspective

@inproceedings{Blum2009ApproximateBC,
  title={Approximate Bayesian Computation : a non-parametric perspective},
  author={Michael G. B. Blum},
  year={2009}
}
Approximate Bayesian Computation is a family of likelihood-free inference techniques that are well suited to models defined in terms of a stochastic generating mechanism. In a nutshell, Approximate Bayesian Computation proceeds by computing summary statistics sobs from the data and simulating summary statistics for different values of the parameter . The posterior distribution is then approximated by an estimator of the conditional density g( |sobs). In this paper, we derive the asymptotic bias… CONTINUE READING
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Multivariate Locally Weighted Least Squares Regression,

  • D. Ruppert, M. P. Wand
  • The Annals of Statistics,
  • 1994
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4 Excerpts

Likelihood-Free Markov Chain Monte Carlo,

  • S. A. Sisson, Y. Fan
  • Handbook of Markov Chain Monte Carlo,
  • 2010
1 Excerpt

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