Papers › Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm

Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm

16 Aug 2016NeurIPS 2016 12arXiv:1608.04471archive 2025-07-28

Qiang Liu, Dilin Wang

We propose a general purpose variational inference algorithm that forms a natural counterpart of gradient descent for optimization. Our method iteratively transports a set of particles to match the target distribution, by applying a form of functional gradient descent that minimizes the KL divergence. Empirical studies are performed on various real world models and datasets, on which our method is competitive with existing state-of-the-art methods. The derivation of our method is based on a new theoretical result that connects the derivative of KL divergence under smooth transforms with Stein's identity and a recently proposed kernelized Stein discrepancy, which is of independent interest.

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