Let’s talk about the Kullback-Leibler divergence. (Sometimes, we call this the “K-L divergence.”) It’s the foundation, the building block, for variational methods. The Kullback-Leibler divergence is a made-up measure. It’s not one of those “fundamental laws of the universe.” It’s strictly a made-up human thing. Nevertheless, it’s become very useful – and is worth our… Continue reading The Kullback-Leibler Divergence, Free Energy, and All Things Variational – Part 1.5 of 3
Tag: Karl Friston
The Kullback-Leibler Divergence, Free Energy, and All Things Variational (Part 1 of 3)
Variational Methods: Where They Are in the AI/ML World The bleeding-leading edge of AI and machine learning (ML) deals with variational methods. Variational inference, in particular, is needed because we can’t envision every possible instance that would comprise a good training and testing data set. There will ALWAYS be some sort of oddball thing that… Continue reading The Kullback-Leibler Divergence, Free Energy, and All Things Variational (Part 1 of 3)