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About this paper
Mehran H. Bazargani, Szymon Urbas, Karl Friston
The authors examine how variational posterior factorization affects online inference and learning in generative models. In a hidden Markov model, common mean-field approximations can yield an approximate ELBO that does not minimize the full variational free energy, while reverse-time posterior factorization enables recursive ELBO calculation from previous-step quantities. This is a mathematical comparison, not experimental evidence of accuracy or speed.
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