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Amortized SVGD (Stein Variational Gradient Descent)

Stein variational gradient descent (SVGD) [1] is a deterministic, gradient-based sampling algorithm for approximate inference. Given a probability density function by a simple iterative update of form.

Compared with Monte Carlo methods, SVGD can achieve good approximation even with a very small number of particles. A simple way to see this is to note that when using only a single particle, which is often found to be a useful approximation in many difficult practical problems.

SVGD with more particles interpolates between gradient descent and approximate inference and provides better uncertainty assessment.


Sources of Amortized SVGD (Stein Variational Gradient Descent):

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