A very basic implementation of SVGD, based on https://github.com/dilinwang820/Stein-Variational-Gradient-Descent
Project description
simpleSVGD
This package is a tiny SVGD algorithm specifically developed to operate on distributions found in HMCLab.
Stein Variational Gradient Descent (SVGD)
SVGD is a general purpose variational inference algorithm that forms a natural counterpart of gradient descent for optimization. SVGD iteratively transports a set of particles to match with the target distribution, by applying a form of functional gradient descent that minimizes the KL divergence.
For more information, please visit the original implementers project website - SVGD, or their publication Qiang Liu and Dilin Wang. Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm. NIPS, 2016.
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