When continuous latent variables are present in our graphical model, one can still use variational inference and learning by maximizing L, where L or L (v, θ, q) represents the evidence lower bound (ELBO) ; however, unlike with discrete latent variables we must now use calculus of variations.

When dealing with continuous latent variables the Mean Field Approximation: $q(\textbf{h}|\textbf{v}) = \prod_{i} q(h_{i}|\textbf{v})$ is fixed at $q(h_{j}|\textbf{v})$ for all j =/= i making the optimal $q(h_{i}|\textbf{v})$ is obtained by normalizing the following unnormalized distribution: $\tilde{q}(h_{i}|\textbf{v})=exp(E_{h-i~ ~ q(h-i|v)} \log{\tilde{p}(\textbf{v},\textbf{h})})$ as long as p does not assign 0 probability to any joint configuration of variables.