Corpus ID: 220055716

Robust and Efficient Approximate Bayesian Computation: A Minimum Distance Approach

@article{Frazier2020RobustAE,
  title={Robust and Efficient Approximate Bayesian Computation: A Minimum Distance Approach},
  author={David T. Frazier},
  journal={arXiv: Methodology},
  year={2020}
}
  • David T. Frazier
  • Published 2020
  • Computer Science, Mathematics, Economics
  • arXiv: Methodology
In many instances, the application of approximate Bayesian methods is hampered by two practical features: 1) the requirement to project the data down to low-dimensional summary, including the choice of this projection, which ultimately yields inefficient inference; 2) a possible lack of robustness to deviations from the underlying model structure. Motivated by these efficiency and robustness concerns, we construct a new Bayesian method that can deliver efficient estimators when the underlying… Expand
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References

SHOWING 1-10 OF 23 REFERENCES
Robust Bayesian Inference via Coarsening
Non-linear regression models for Approximate Bayesian Computation
On the Asymptotic Efficiency of Approximate Bayesian Computation Estimators
Approximate Bayesian computation with the Wasserstein distance
Likelihood-free Bayesian estimation of multivariate quantile distributions
Asymptotic properties of approximate Bayesian computation
Model misspecification in approximate Bayesian computation: consequences and diagnostics
An adaptive sequential Monte Carlo method for approximate Bayesian computation
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