Approximate Inference with Amortised MCMC

@article{Li2017ApproximateIW,
  title={Approximate Inference with Amortised MCMC},
  author={Yingzhen Li and Richard E. Turner and Qiang Liu},
  journal={CoRR},
  year={2017},
  volume={abs/1702.08343}
}
We propose a novel approximate inference algorithm that approximates a target distribution by amortising the dynamics of a user-selected MCMC sampler. The idea is to initialise MCMC using samples from an approximation network, apply the MCMC operator to improve these samples, and finally use the samples to update the approximation network thereby improving its quality. This provides a new generic framework for approximate inference, allowing us to deploy highly complex, or implicitly defined… CONTINUE READING
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