variational inference
Variational inference is a computational technique used in machine learning and statistics for approximating complex probability densities. It involves formulating a simpler distribution that closely approximates the true distribution of interest. This approximation is achieved by minimizing the difference between the true distribution and the simpler one, typically using optimization methods. Variational inference provides a way to efficiently estimate posterior distributions, which are key in tasks such as Bayesian inference and probabilistic modeling.
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