Corpus ID: 203656839

Likelihood-free MCMC with Amortized Approximate Likelihood Ratios

@article{Hermans2019LikelihoodfreeMW,
  title={Likelihood-free MCMC with Amortized Approximate Likelihood Ratios},
  author={J. Hermans and Volodimir Begy and Gilles Louppe},
  journal={arXiv: Machine Learning},
  year={2019}
}
Posterior inference with an intractable likelihood is becoming an increasingly common task in scientific domains which rely on sophisticated computer simulations. Typically, these mechanistic models do not admit tractable densities forcing practitioners to rely on approximations during inference. This work proposes a novel approach to address the intractability of the likelihood and the marginal model. We achieve this by learning a flexible estimator which approximates the likelihood-to… Expand
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