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If you find the conflict around frequentist and bayesian methods entertaining check out Jaynes' Theory of Probability, which profiles lots of great arguments and personality profiles. Also you might learn a few things about maxent, statistical physics, and my favorite: the mind-projection fallacy.

I can't find the book with that title, do you have a link or ISBN? http://www.amazon.com/Probability-Theory-Science-T-Jaynes/dp... is the closest match but it does not seem to match your description.

Thanks and sorry--I misrecollected. The correct title is Probability Theory: The Logic of Science.

I've got a digital copy around here somewhere. Send me a message if you want.

Hi jcarden, i wouldnt mind a copy, where can i message you to.

Thanks !

I think it's not exactly the same as the book version, but the author's original is available at http://www-biba.inrialpes.fr/Jaynes/prob.html

Does anyone have a combined PDF? While I appreciate the author putting out the PDF, separating the files out into individual chapters and figures makes it harder to read in a sitting.

that's great, thank you, this will suffice for now :)

At what level is the presentation of statistics in his book? I'm interested to learn more about the application of statistics and Bayesian methods (as a physics major I intend to deal with lots of data), but I haven't taken formal courses on statistics yet.

(Of course, being a physics major, I don't mind having to hurt my brain a bit to get through it, so long as it's good.)

I've recently started going through http://www.amazon.com/Data-Analysis-Bayesian-Devinderjit-Siv... and can highly recommend it. The benefits of Bayesian reasoning can be grasped somewhat easily without a detailed knowledge of the math, but at the end of the day scientists and engineers still need to learn how to use it and write programs using it to do stuff! One review mentions the book dives right into the subject, it does; I believe Bayes' Theorem is shown on page 6. Some people find a brisk pace hard to follow, personally I like it since I can always fill in the gaps with other material if needed. (Like Jaynes, or http://uncertainty.stat.cmu.edu/ )

Basic conditional probability understanding is sufficient.

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