A package accompanying https://arxiv.org/abs/2204.09039, which implements Stein variational gradient descent (SVGD), Stein variational Newton (SVN), and their stochastic counterparts (sSVGD, sSVN).
Stein variational gradient descent (SVGD) is a general-purpose optimization-based sampling algorithm that has recently exploded in popularity, but is limited by two issues: it is known to produce biased samples, and it can be slow to converge on complicated distributions. A recently proposed stochastic variant of SVGD (sSVGD) addresses the first issue, producing unbiased samples by incorporating a special noise into the SVGD dynamics such that asymptotic convergence is guaranteed. Meanwhile, Stein variational Newton (SVN), a Newton-like extension of SVGD, dramatically accelerates the convergence of SVGD by incorporating Hessian information into the dynamics, but also produces biased samples. In this paper we derive, and provide a practical implementation of, a stochastic variant of SVN (sSVN) which is both asymptotically correct and converges rapidly. We demonstrate that this method holds promise for parameter estimation problems of modest dimension.
Code for these animations found in notebooks/double_banana_flows.ipynb
Code has been tested using Python 3.6 on both Linux and Windows. Install libraries in requirements.txt and run a notebook of your choice from /notebooks.