For people who've read it, do you have a review of Okasaki's book? Also, what background should someone have before reading it (eg should you be able to read it after reading Learn You a Haskell? Should you already be familiar with the imperative equivalents?)
Short answer: I don't think LYAH would be entirely sufficient background, and I think familiarity with imperative equivalents is a prerequisite, but along with some other background.
Long version: I stumbled a bit when trying to read this book, because I'm not super comfortable with the formal techniques for analyzing algorithms. I'm not talking about the kind of fluffy-tech-company-interview-"knows what big Oh is and can estimate it roughly on a whiteboard" ability but actually something much more concrete and formal, the kind taught in an undergrad algorithms class or in CLRS. I've taken those classes, I have CLRS on my shelf and have read some of it, but it's been a long time and it's not at my fingertips. There are exercises in the book ("prove that this data structure has this amortized performance", "prove that it does if you change this one thing", etc...) that challenged me quite a bit, and I ended up putting it down after a while thinking I would revisit it after refreshing my fundamentals.
The ML code is easy enough to follow if you know Haskell, but since the point is often to understand the subtlety of the performance characteristics of the data structures and algorithms, just reading and playing with the code wasn't enough for me. After all you can trivially implement functional data structures naively just by copying everything on every operation.
Frankly, Rich Hickey's videos on Clojure's data structures have done a lot more for me in terms of understanding how functional data structures work, even though they are much less formal in presentation. To be clear: I think Okasaki's book is probably a very good book and an important work, but am not so sure it is crucial for the working functional programmer to internalize all the mathematical details.
Anyway, YMMV. Check out his thesis and see what you think before buying it.
It's outstanding. My favorite bit is the section on building data structures with various characteristics inspired by obscure number systems (zeroless binary numbers, skew binary numbers...).
I'd recommend some experience with complexity analysis (though "ages ago in school" is probably fine). I found most of the SML examples to be reasonably transparent, though I did some OCaml ages ago and have been working with Haskell more recently. He uses laziness heavily, but it's explicit. Knowing your way around a few imperative data structures is almost certainly worthwhile; I'm not positive it's strictly necessary.
