Variational Inference in Location-Scale Families: Exact Recovery of the
Mean and Correlation Matrix
Variational Inference in Location-Scale Families: Exact Recovery of the
Mean and Correlation Matrix
Given an intractable target density $p$, variational inference (VI) attempts to find the best approximation $q$ from a tractable family $Q$. This is typically done by minimizing the exclusive Kullback-Leibler divergence, $\text{KL}(q||p)$. In practice, $Q$ is not rich enough to contain $p$, and the approximation is misspecified even when it …