Thomas Parr

“Sometimes, the use of free energy is motivated directly in terms of this divergence. The argument goes that if our aim is to perform approximate Bayesian inference, we need to find an approximate posterior that best matches the exact posterior. As such, we can select a measure of the divergence between the two—of which the KL-Divergence in equation 4.4 is one example—and minimize this. As we do not know the exact posterior, we cannot use this divergence directly. One solution is to add the log evidence term, which may be combined with the log posterior to form the joint probability (which we do know because this is the generative model). The result is the free energy.”
― Thomas Parr, Active Inference: The Free Energy Principle in Mind, Brain, and Behavior