Deep probabilistic programming is a method of implementing “Bayesian” probabilistic modeling on “differentiable” deep learning frameworks. This provides a style of language to define complex (composite, hierarchical) models with multiple components and incorporate probabilistic uncertainty about latent variables or model parameters into predictions.

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