This is a good one. I definitely sympathize with Eliezer's point that Bayesian probability theory is only part of the solution. e.g., in philosophy of science, the deductive-nomological account of scientific explanation is being displaced by a mechanistic view of explanation. In this context, a mechanism is an organization of parts which is responsible for some phenomena. This change is driven by the inapplicability of D-N to certain areas of science, especially the biomedical sciences, where matters are more complex and we can't really deduce conclusions from universal laws; instead, people are treating law-like regularity as phenomena to be explained by appeal to the organized interactions of underlying parts.

e.g., Instead of explaining, "You display symptoms Y; All people with symptoms Y have disease X; Therefore, you have disease X," mechanists explain by positing a mechanism, the functioning of which constitutes the phenomena to be explained. This seems to me to be intimately related to Eliezer's "reduce-to-algorithm" stance; and that an appeal to reduce abstract beliefs to physical mechanisms seems to be a pretty good way to generalize his stance here. In addition, certain mechanistic philosophers have done work to connect mechanisms and mechanistic explanation with Bayesian probability, and with Pearl's work on Bayesian networks and causality. Jon Williamson at Kent has my favorite account: he uses Recursive Bayesian Networks to model this sort of mechanistic thinking quantitatively.