Machine Learning, etc

I found a book scanner in our library's basement and decided to put it to good use by scanning some hard-to-find-online references on foundations of Bayesianism.

• "Algebra of Probable Inference" by Cox, 1961 (aka, Why everyone should be a Bayesian). Demonstrates a functional derivation of probability theory as the unique extension of Boolean Algebra.
• "Why I'm not a Bayesian" by Clark Glymour, Theory and Evidence, 1981. Criticizes Bayesian approach from the philosophy of science point of view.
  
• "Why Glymour is a Bayesian" by R Rosenktrantz, Testing Scientific Theories, 1983
• "Why isn't everyone a Bayesian?" by Efron B, American Statistician 1986. Examines reasons why not everybody was a Bayesian, as of 1986, with scorching reply from Lindley.
• "Axioms of Maximum Entropy" by Skilling, MaxEnt 1988 proceedings. Sets up four practically motivated axioms, and uses them to derive maximum entropy as the unique method for picking a single probability distribution from the set of valid probability distributions.

I included the last one because there are some parallels with philosophy of Bayesianism. Following Cox, one should be a Bayesian if

1. They assume boolean logic
2. Can encode their true belief as a pdf
   

On other hand, following Skilling, one should use ME principle if they

1. Believe Skilling's axioms.
2. Have statements about true distribution in the form of constraints.

In both models assumption number 1 sounds more or less reasonable, whereas assumption number 2 causes discontent. With Bayesian approach, we don't know how to represent our prior knowledge as a pdf, whereas with ME approach, we don't know where to get the constraints from. However, in practice, we can often find a pdf that is close to representing the true belief of the expert, and similarly we can often find constraints that approximately rule out unfeasible distributions.

Questions

• The references are over 20 years old. One newer one from 2000 by Berger looks at rising popularity of "objective" Bayesian and robust Bayesian approaches, and predicts practical Bayesianism of the future to contain both frequentist and traditional Bayesian elements. Does anyone know of more up-to-date overviews of different inductive reasoning methods?

BTW, if you are the author, and don't like the links to your work, let me know, and I'll remove them