The main goal of deep learning and statistical modeling is to encode the dependencies between variables. By getting an idea of those dependencies, from the values of the known variables, a model can answer questions about the unknown variables.
Energy-based models (EBMs) [120] gather and collect the dependencies by identifying scaler energy, which generally is a measure of compatibility to each configuration of the variable. In EBMs, the predictions are made by setting the value of observed variables and finding the value of the unobserved variables, which minimize the overall energy. Learning in EBMs consists of formulating an energy function, which assigns low energies to the correct values of unobserved variables and higher energies to the incorrect ones. Energy-based learning can be treated as an alternative to probabilistic estimation for classification, decision-making, or prediction tasks.
To give a clear idea about how EBMs work, let us look at a simple example...
