• Corpus ID: 204800956

Amortized Rejection Sampling in Universal Probabilistic Programming

@inproceedings{Naderiparizi2019AmortizedRS,
  title={Amortized Rejection Sampling in Universal Probabilistic Programming},
  author={Saeid Naderiparizi and A. Scibior and Andreas Munk and Mehrdad Ghadiri and Atilim Gunes Baydin and Bradley Gram-Hansen and C. S. D. Witt and Robert Zinkov and Philip H. S. Torr and Tom Rainforth and Yee Whye Teh and Frank D. Wood},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  year={2019}
}
Existing approaches to amortized inference in probabilistic programs with unbounded loops can produce estimators with infinite variance. An instance of this is importance sampling inference in programs that explicitly include rejection sampling as part of the user-programmed generative procedure. In this paper we develop a new and efficient amortized importance sampling estimator. We prove finite variance of our estimator and empirically demonstrate our method's correctness and efficiency… 

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