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This document introduces a simple approach for embedding probabilistic programming in deep learning ecosystems called Edward2. It distills probabilistic programming down to a single abstraction - the random variable. This lightweight design in TensorFlow enables flexible modeling while maintaining tight integration with numerical libraries. It allows arbitrary computation during training rather than restricting algorithms to "inference queries". The authors demonstrate three applications using this approach

Author

M Feldy Riza

Language

EN