Improving Automated Variational Inference with Normalizing Flows by Stefan Webb, J. P. Chen, Martin Jankowiak and Noah Goodman
We describe a framework for performing automatic Bayesian inference in probabilistic programs with fixed structure. Our framework takes a probabilistic program with fixed structure as input and outputs a learnt variational distribution approximating the posterior. For this purpose, we exploit recent advances in representing distributions with neural networks. We implement our approach in the Pyro probabilistic programming language, and validate it on a diverse collection of Bayesian regression models translated from Stan, showing improved inference and predictive performance relative to the existing state-of-the-art in automated inference for this class of models.
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