An approximate inference approach that recasts posterior inference as optimization: a tractable family of distributions is fit to the true posterior by minimizing KL divergence (equivalently, maximizing the ELBO). Trades exactness for scalability; the dominant approach for very large datasets and high-dimensional latent-variable models where MCMC is prohibitive.