This website contains materials introducing Metropolis-Hastings SVGD (MET-SVGD), a novel variational inference framework designed for efficient and scalable entropy estimation. MET-SVGD builds on ideas from Stein Variational Gradient Descent (SVGD), parametric variational inference (P-VI), and Metropolis-Hastings (MH), combining their strengths to offer a flexible and efficient approach to approximating complex distributions.
Besides an introduction to SVGD, variational inference, and related topics, we also provide documentation and code examples for the library accompanying the project.
Section 1: Introduction
Section 2: SVGD
Section 3: The SVGD-Induced Density
Section 4: MET-SVGD Optimizations
Section 5: Results
Section 6: Library Documentation