Deep Learning Systems
Variational inference is a method in probabilistic modeling that approximates complex posterior distributions by transforming them into simpler distributions. This approach allows for efficient computation and optimization, enabling the use of variational methods in machine learning tasks, such as generative modeling and latent variable models.
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