>Probabilistic models must be properly normalized, which sometimes requires evaluating intractable integrals over the space of all possible variable configurations. Since EBMs have no requirement for proper normalization, this problem is naturally circumvented. EBMs can be viewed as a form of non-probabilistic factor graphs, and they provide considerably more flexibility in the design of architectures and training criteria than probabilistic approaches.

Can someone explain to me what the major difference is between energy-based models and variational Bayes approximations, which throw out calculating the normalization constant and switch to maximizing the log joint probability of the data and model?