Corpus ID: 53294779

Neural Likelihoods via Cumulative Distribution Functions

@article{Chilinski2020NeuralLV,
  title={Neural Likelihoods via Cumulative Distribution Functions},
  author={Pawel M. Chilinski and Ricardo Silva},
  journal={ArXiv},
  year={2020},
  volume={abs/1811.00974}
}
  • Pawel M. Chilinski, Ricardo Silva
  • Published 2020
  • Computer Science, Mathematics
  • ArXiv
  • We leverage neural networks as universal approximators of monotonic functions to build a parameterization of conditional cumulative distribution functions. By a modification of backpropagation as applied both to parameters and outputs, we show that we are able to build black box density estimators which are competitive against recently proposed models, while avoiding assumptions concerning the base distribution in a mixture model. That is, it makes no use of parametric models as building blocks… CONTINUE READING

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