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Comment by zbuc | Hacker News Reader

I would absolutely love to learn more. I've been trying to solve some novel NLP and machine learning problems lately but my lack of statistical knowledge is becoming apparent the further along I get.

Do you have any recommendations for a good introductory treatment of Bayesian statistics?

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By far my favorite book on the subject is conveniently available for free on the internet! "Information Theory, Inference and Learning Algorithms" by David MacKay is wonderfully written, well paced and comprehensive. If you like the book, you should purchase a copy, David is a great guy.

http://www.inference.phy.cam.ac.uk/mackay/itila/book.html

Videolectures has some very good videos as well. Zoubin Gharamani has a pretty solid lecture on Bayesian learning at http://videolectures.net/mlss05us_ghahramani_bl/ (he's a great researcher but not the most engaging speaker). Try Christopher Bishop's lecture at http://videolectures.net/mlss09uk_bishop_ibi/ as well, it might be slightly more palatable.

I want to second MacKay's book. I had a terrible statistics class in college. We spent the entire time looking up tables of p-values and t tests, without a very convincing explanation as to why. The entire topic was damaged for me from then on until I read MacKay online, and then bought the paper version.

His book starts from first principles -- simple ideas about probabilities -- and it builds a foundation for understanding Bayesian methods. And he explains, basically, why the p-values and t-test stuff is a bunch of crap, which was an immense relief.

So I don't need to flinch over the mention of "p hat" again?

Thanks for the resource, this looks fantastic.

Thank you very much for the recommendations.

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