Variational Inference
Variational Inference (VI) is a statistical method for approximating complex probability distributions, particularly in Bayesian inference. It transforms the problem of computing intractable posterior distributions into an optimization task by finding the best approximation from a simpler family of distributions. This technique is widely used in machine learning for probabilistic models where exact inference is computationally infeasible.
Developers should learn Variational Inference when working with Bayesian models, deep generative models (like VAEs), or any probabilistic framework where exact posterior computation is too slow or impossible. It's essential for scalable inference in large datasets, enabling applications in natural language processing, computer vision, and unsupervised learning by providing efficient approximations with trade-offs in accuracy.