Non-linear regression models for Approximate Bayesian Computation

  title={Non-linear regression models for Approximate Bayesian Computation},
  author={Michael G. B. Blum and Olivier François},
  journal={Statistics and Computing},
Approximate Bayesian inference on the basis of summary statistics is well-suited to complex problems for which the likelihood is either mathematically or computationally intractable. However the methods that use rejection suffer from the curse of dimensionality when the number of summary statistics is increased. Here we propose a machine-learning approach to the estimation of the posterior density by introducing two innovations. The new method fits a nonlinear conditional heteroscedastic… 
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  • J. HeyR. Nielsen
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