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tl;dr - medium grain statistical review from Classic (frequentist, Bayesian) to Modern (Monte Carlo, support vector machines)I may never actually finish reading this book. That's okay because I don't think this is the kind of book you just read. I and several folk I work with are making our way through it, using data from their website with the figures as targeted examples for our R scripts. It's a phenominal resource for showing the flow of thinking about statistics, from the 'strictly pen and [...]

Brings forward an amazing clarity, its an experience worth going through. (Of course, given that I am an extremely mediocre person, using Google helped me get the most out of this book).

Quite good.

Two experts from Stanford have written a great historical/philosophical/mathematical overview of modern statistics that compares and contrasts frequentist, Bayesian and computer intensive algorithmic approaches to data analysis. I've fooled with all this stuff, but it's a pleasure having professors this smart tie it all together. It seems like it must have been an Herculean task. There are good examples and a very small amount of R code. The book, from Cambridge U press, is also very well produc [...]

This is a little like Larry Wasserman's books in breadth.