Projected Stein Variational Gradient Descent

The curse of dimensionality is a critical challenge in Bayesian inference for high dimensional parameters. In this work, we address this challenge by developing a projected Stein variational gradient descent (pSVGD) method, which projects the parameters into a subspace that is adaptively constructed using the gradient of the log-likelihood, and applies SVGD for the much lower-dimensional coefficients of the projection. We provide an upper bound for the projection error with respect to the posterior and demonstrate the accuracy (compared to SVGD) and scalability of pSVGD with respect to the number of parameters, samples, data points, and processor cores.