Eliezer, what is your view of the relationship between Bayesian Networks and Solomonoff Induction? You’ve talked about both of these concepts on this blog, but I’m having trouble understanding how they fit together. A Google search for both of these terms together yields only one meaningful hit, which happens to be a mailing list post by you. But it doesn’t really touch on my question.
On the face of it, both Bayesian Networks and Solomonoff Induction are “Bayesian”, but they seem to be incompatible with each other. In the Bayesian Networks approach, conditional probabilities are primary, and the full probability distribution function is more of a mathematical formalism that stays in the background. Solomonoff Induction on the other hand starts with a fully specified (even if uncomputable) prior distribution and derives any conditional probabilities from it as needed. Do you have any idea how to reconcile these two approaches?
Eliezer, what is your view of the relationship between Bayesian Networks and Solomonoff Induction? You’ve talked about both of these concepts on this blog, but I’m having trouble understanding how they fit together. A Google search for both of these terms together yields only one meaningful hit, which happens to be a mailing list post by you. But it doesn’t really touch on my question.
On the face of it, both Bayesian Networks and Solomonoff Induction are “Bayesian”, but they seem to be incompatible with each other. In the Bayesian Networks approach, conditional probabilities are primary, and the full probability distribution function is more of a mathematical formalism that stays in the background. Solomonoff Induction on the other hand starts with a fully specified (even if uncomputable) prior distribution and derives any conditional probabilities from it as needed. Do you have any idea how to reconcile these two approaches?