It differs from other Bayesian approximation approaches such as variational Bayesian methods. The derivation is a simple variational calculation using Lagrange multipliers. This type of assumption underlies the variational Bayesian method. By applying variational techniques from classical mechanics, one can also regard geodesics as Hamiltonian flows. Any physical law which can be expressed as a variational principle describes a self-adjoint operator. A variational principle is used to determine the equilibrium density. It was first designed as a model for brain functioning using variational Bayesian learning. Examples of variational formulation are the Galerkin method, the discontinuous Galerkin method, mixed methods, etc. This result may also be demonstrated using the variational calculus. Singular values are similar in that they can be described algebraically or from variational principles. There are other approximate methods for marginalization including variational methods and Monte Carlo methods. When is Hermitian, a variational characterization is also available. To solve the controllability problem, many works about variational auto-encoder are proposed. Leibler divergence between the variational and posterior density over hidden states is minimised. Newton himself was thinking along the lines of the variational calculus, but did not publish.