An Introduction to Variational Inference
This is an introductory paper explaining VI for researchers in statistics and machine learning, making it incremental as it reviews existing concepts.
The paper tackles the problem of approximating complex probability densities by introducing Variational Inference (VI), a method that uses optimization to estimate these densities, resulting in faster convergence compared to classical methods like Markov Chain Monte Carlo sampling.
Approximating complex probability densities is a core problem in modern statistics. In this paper, we introduce the concept of Variational Inference (VI), a popular method in machine learning that uses optimization techniques to estimate complex probability densities. This property allows VI to converge faster than classical methods, such as, Markov Chain Monte Carlo sampling. Conceptually, VI works by choosing a family of probability density functions and then finding the one closest to the actual probability density -- often using the Kullback-Leibler (KL) divergence as the optimization metric. We introduce the Evidence Lower Bound to tractably compute the approximated probability density and we review the ideas behind mean-field variational inference. Finally, we discuss the applications of VI to variational auto-encoders (VAE) and VAE-Generative Adversarial Network (VAE-GAN). With this paper, we aim to explain the concept of VI and assist in future research with this approach.