Component-wise approximate Bayesian computation via Gibbs-like steps

@article{Clarte2019ComponentwiseAB,
  title={Component-wise approximate Bayesian computation via Gibbs-like steps},
  author={Gr'egoire Clart'e and C. Robert and R. Ryder and Julien Stoehr},
  journal={arXiv: Computation},
  year={2019}
}
  • Gr'egoire Clart'e, C. Robert, +1 author Julien Stoehr
  • Published 2019
  • Mathematics
  • arXiv: Computation
  • Approximate Bayesian computation methods are useful for generative models with intractable likelihoods. These methods are however sensitive to the dimension of the parameter space, requiring exponentially increasing resources as this dimension grows. To tackle this difficulty, we explore a Gibbs version of the ABC approach that runs component-wise approximate Bayesian computation steps aimed at the corresponding conditional posterior distributions, and based on summary statistics of reduced… CONTINUE READING
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    References

    SHOWING 1-10 OF 24 REFERENCES
    Asymptotic properties of approximate Bayesian computation
    • 52
    • PDF
    Sampling-Based Approaches to Calculating Marginal Densities
    • 6,631
    • PDF
    ABC random forests for Bayesian parameter inference
    • 59
    • PDF
    Constructing Summary Statistics for Approximate Bayesian Computation: Semi-automatic ABC
    • 174
    • PDF
    Approximate Bayesian computational methods
    • 613
    • PDF
    Approximate Bayesian computation in population genetics.
    • 2,337
    • PDF
    Pre-processing for approximate Bayesian computation in image analysis
    • 30
    • PDF