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Variational Bayesian methods approximate intractable integrals found in Bayesian inference and machine learning. Primarily, these methods serve one of two purposes: Approximating the posterior distribution, or bounding the marginal likelihood of observed data.

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Varational inference - coordinate ascent question

I just came here having the same question. After spending some time to figure out what the funtional derivatives is, I find that the core idea here is the "local changes of the funcion" in Calculus of …
yang piao's user avatar
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How to maximize the ELBO in coordinate ascent variational inference

From (22) to (23), it involves functional derivatives. The same question can be found here. From (23) to (24), it just set (23) equals to 0 and get the corresponding $q_j(z_j)$. The constant in the ex …
yang piao's user avatar