In variational inference and learning, inference is framed as an optimization problem where the evidence lower bound (ELBO), denoted as $\mathcal{L}$, is maximized with respect to an approximate distribution $q$, and learning is achieved by maximizing $\mathcal{L}$ with respect to the model parameters $\theta$. The equations for the ELBO are $\mathcal{L}(v,\theta,q)= \log p(v;\theta) - D_{KL}(q(h|v)||p(h|v))$ and $\mathcal{L}(v,\theta,q)= \mathbb{E}_{h\sim q}(\log p(h,v))+H(q)$. Because maximizing $\mathcal{L}$ with respect to $\theta$ without restrictions is often intractable, learning is performed over a restricted family of possible distributions for $q$. This restricted set is chosen to allow for the tractable computation of $\mathbb{E}_{h\sim q}(\log p(h,v))$.