Any book on statistics needs to have a section about regularization/complexity control and the relationship to generalization. This is an enormous lacuna in standalone Bayesian philosophy. I now see that most of Jaynes' effort in the book is an attempt to repair the essential problem with Bayesian statistics, which is the subjectivity of the prior. In particular, Jaynes believed that the MaxEnt idea provided a way to derive a prior directly from the problem formulation. I believe he failed in his effort. The prior is intrinsically subjective and there is no way to get around this in the traditional small-N data regime of statistics. Two observers, looking at the same small data set, can justifiably come to very different conclusions. Objectivity is only re-achieved when the size of the data set becomes large, so that Solomonoff-style reasoning can be used.