<ul><li>This paper presents a method for approximating the inverse of the Fisher information matrix in variational Bayes inference.</li><li>The approach avoids computing the Fisher information matrix analytically and its explicit inversion.</li><li>Instead, an iterative procedure generates a sequence of matrices that converge to the inverse of Fisher information.</li><li>The proposed algorithm achieves a convergence rate of O(log s/s) and exhibits versatility across various variational Bayes domains.</li></ul>

Research on inner workings of Variational Bayes method part4(Statistical Machine Learning)

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