Component-wise approximate Bayesian computation via Gibbs-like steps

  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},
  • 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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