Stein Variational Gradient Descent: Theory and Applications
نویسنده
چکیده
Although optimization can be done very efficiently using gradient-based optimization these days, Bayesian inference or probabilistic sampling has been considered to be much more difficult. Stein variational gradient descent (SVGD) is a new particle-based inference method derived using a functional gradient descent for minimizing KL divergence without explicit parametric assumptions. SVGD can be viewed a natural counterpart of gradient descent for optimization, and in fact exactly reduces to the typical gradient ascent for MAP using only a single particle. This short paper gives a brief introduction to SVGD, and discusses its theoretical foundation and applications.
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