That’s a horrible cacophony of analogies in service of saying that no number of any of us, let alone any one of us, has found all that’s to be found about probability or analysis or number theory or whatever. Still, a good book can make inching into any one field far easier, or at least more time-efficient. It is not without irony then that I’d land on a book ambitiously titled All of Statistics: A Concise Course in Statistical Inference.

Probability and statistics in their various guises are intrinsically interesting but most every book I’ve looked through is either a lamentable collection of recipes with no theoretical footing or is steeped in notation and a tiring venture into theory that makes self-study (especially the nocturnal sort) daunting. No problem with either, except when you need something else. The opening pages of All of Statistics speaks to this very conundrum:

[W]here can students learn basic probability and statistics quickly? Nowhere. At least, that was my conclusion when my computer science colleagues kept asking me: “Where can I send my students to get a good understanding of modern statistics quickly?” The typical mathematical statistics course spends too much time on tedious and uninspiring topics (counting methods, two-dimensional integrals, etc.) at the expense of covering modern concepts (bootstrapping, curve estimation, graphical models, etc.). So I set out to redesign our undergraduate honors course on probability and mathematical statistics. This book arose from that course.

That’s pretty happy-making. This part also really juiced me up:

The book is suitable for graduate students in computer science and honors undergraduates in math, statistics, and computer science. It is also useful for students beginning graduate work in statistics who need to fill in their background on mathematical statistics.

While I’m not, and may never, do any graduate work as such, I’m definitely working toward a similar level of theoretical mastery on the way to applying it.

Early days yet but this looks promising.

☐