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.
I've been reading this. The explanations are good and the exercises are interesting, but I can't find any form of solutions manual(if there is one please let me know). This is a big drawback if you want to use it for self/independent study.
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.