If only this book had some more examples of applications, it be a contender for 'best introductory textbook for statistics'. As it stands, it makes a great complement to either Wasserman's All of Statistics (filling in the Bayesian side of things) or Gelman, Carlin, Rubin, Stern's Bayesian Data Analysis (filling in theoretical side of things.) There has been a huge need for a 'Jaynes-lite' which offers the philosophical grounding of P:tLoS sans its distracting (and now outdated) polemics.
How does this compare to Data Analysis: A Bayesian Tutorial? In any case, you should post your suggestion in the Best Textbooks thread.
Joseph Kadane, emeritus at Carnegie Mellon, released his new statistics textbook Principles of Uncertainty as a free pdf. The book is written from a Bayesian perspective, covering basic probability, decision theory, conjugate distribution analysis, hierarchical modeling, MCMC simulation, and game theory. The focus is mathematical, but computation with R is touched on. A solid understanding of calculus seems sufficient to use the book. Curiously, the author devotes a fair number of pages to developing the McShane integral, which is equivalent to Lebesgue integration on the real line. There are lots of other unusual topics you don't normally see in an intermediate statistics textbook.
Having came across this today, I can't say whether it is actually very good or not, but the range of topics seems perfectly suited to Less Wrong readers.