Stein Variational Gradient Descent (SVGD) is an algorithm used for Bayesian inference. It utilizes Stein’s Lemma, Variational Inference, and Kernel Density Estimation to estimate the posterior distribution. SVGD applies gradients to iteratively modify a distribution until it aligns with the target posterior distribution. It finds applications in Bayesian neural networks and statistical machine learning. Researchers like Matthew D. Hoffman and David M. Blei have contributed significantly to this field. TensorFlow Probability and PyTorch provide software tools for SVGD implementation. Alternative methods such as Metropolis-Hastings Algorithm, Gibbs Sampling, Hamiltonian Monte Carlo, and Langevin Dynamics offer different approaches to Bayesian inference and gradient estimation.
