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Topic:variational inference

Variational inference is a technique used in statistical modeling and machine learning to approximate complex probability distributions. Instead of explicitly calculating the exact posterior distribution, variational inference aims to find an approximation that is as close as possible to the true distribution. This is done by framing the problem as an optimization task, where a simpler distribution (such as a Gaussian distribution) is chosen as the variational family and adjusted to minimize the difference with the true posterior distribution. Variational inference is widely used in Bayesian inference and has applications in various fields such as natural language processing, computer vision, and reinforcement learning.