Inference Networks for Sequential Monte Carlo in Graphical Models

@inproceedings{Paige2016InferenceNF,
  title={Inference Networks for Sequential Monte Carlo in Graphical Models},
  author={Brooks Paige and Frank D. Wood},
  booktitle={ICML},
  year={2016}
}
We introduce a new approach for amortizing inference in directed graphical models by learning heuristic approximations to stochastic inverses, designed specifically for use as proposal distributions in sequential Monte Carlo methods. We describe a procedure for constructing and learning a structured neural network which represents an inverse factorization of the graphical model, resulting in a conditional density estimator that takes as input particular values of the observed random variables… CONTINUE READING
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