I'm not sure what your background is; there are a number of books in philosophy of statistics that might be more accessible than Jaynes. I'm with the others - you should study some of the simpler foundations of statistics before diving into Bayesianism.

1) Luce and Raiffa's Games and Decisions (1989) introduces von Neumann and Morgenstern's axioms of objective utility theory and game theory; the book is geared towards serious social scientists.

2) Savage's The Foundations of Statistics (1954) is much harder than Luce and Raiffa, but it introduces an axiomatic system of subjective utility theory that complements objective utility theory well.

3) Fagin, Halpern, Moses and Vardi's Reasoning About Knowledge (2004) contains a reasonable introduction to statistics via the Kolmogorov axiomatization. It is suitable for technically minded philosophy students who have no background in measure theory, set theory or analysis.

4) Modern statistics builds heavily on calculus. You may want to brush up - I really like Bressoud's A Radical Approach to Real Analysis (2006). If you find yourself into the Kolmogorov axiomatization, you should also check out Bressoud's A Radical Approach to Lebesgue Measure Theory (2008).

These books should give you some perspective on the different traditions in philosophy of statistics and rational decision theory. After this Jaynes should make a lot of sense - he belongs to the "subjective" camp that Savage pioneered. However, it is important to understand when studying philosophy of statistics that there are multiple camps and they have different perspectives.