Corpus ID: 16152464

Deep Amortized Inference for Probabilistic Programs

@article{Ritchie2016DeepAI,
  title={Deep Amortized Inference for Probabilistic Programs},
  author={Daniel Ritchie and Paul Horsfall and Noah D. Goodman},
  journal={ArXiv},
  year={2016},
  volume={abs/1610.05735}
}
  • Daniel Ritchie, Paul Horsfall, Noah D. Goodman
  • Published 2016
  • Computer Science, Mathematics
  • ArXiv
  • Probabilistic programming languages (PPLs) are a powerful modeling tool, able to represent any computable probability distribution. Unfortunately, probabilistic program inference is often intractable, and existing PPLs mostly rely on expensive, approximate sampling-based methods. To alleviate this problem, one could try to learn from past inferences, so that future inferences run faster. This strategy is known as amortized inference; it has recently been applied to Bayesian networks and deep… CONTINUE READING

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