Reddit reviews Introduction to Bayesian Statistics, 2nd Edition
We found 4 Reddit comments about Introduction to Bayesian Statistics, 2nd Edition. Here are the top ones, ranked by their Reddit score.
Here's my favorite general, theoretical intro to Bayesian stats, by the author of the logic of science book above. Interesting to read and not too long-
http://bayes.wustl.edu/etj/articles/general.background.pdf
More...This one tries to re-teach stats from square one. It's alright, but stops short of Markov Chain Monte Carlo, which is where things get fun.
http://www.amazon.co.uk/Introduction-Bayesian-Statistics-William-Bolstad/dp/0470141158/ref=sr_1_1?ie=UTF8&s=books&qid=1280142090&sr=1-1
This is the one I'm reading now, which explains bayes for people in the social sciences, and makes an effort to break down the cool stuff into simple terms. I really like the writer and its good so far-
http://www.amazon.co.uk/Bayesian-Methods-Behavioral-Sciences-Statistics/dp/1584885629/ref=sr_1_2?ie=UTF8&s=books&qid=1280142179&sr=1-2
> This is a false idea, unless you know the standard deviation of the dataset.
Bayesian statistics bro. Here's a good book
Also, to extrapolate solely from the standard deviation, I would have to believe the distribution is normal. I have no reason to believe such a thing, and neither do you. A distribution would be an interesting measurement to cite, but seeing as you couldn't cite either the percentage you claimed in Boston (15%) nor the nationwide standard deviation, I doubt you have any information on the distribution.
>"Gödel, Escher, Bach" by Douglas R. Hofstadter is the most awesome book that I have ever read. If there is one book that emphasizes the tragedy of Death, it is this book, because it's terrible that so many people have died without reading it."
Apparently I never got remotely far-enough into the book for this statement to make sense.
(I got tired of carrying that huge paperback around in my backpack.)
Lemme go get a Kindle copy.
I've heard good things about Good and Real.
>Artificial Intelligence: A Modern Approach[2] , also recommended by Yudkowsky, is the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications. It's the leading textbook in the field of artificial intelligence, used in over 1100 universities worldwide. I think it's obvious why a community read-through of this would be beneficial.
Russel and Norvig is the standard textbook for "Good Old-Fashioned AI", ie: the kind that's not at all worthy of being called "AI". It's used as a textbook in the first course in GOFAI for undergrads. It teaches fairly little programming, very little mathematics, and covers nothing of the kind of modern machine-learning techniques that actually get results these days, let alone the increasingly elegant and advanced learning techniques that are yielding good models of what cognition is.
On the textbook front, though, I can recommend that anyone with basic Calc 1+2 under their belt can go ahead and read Introduction to Bayesian Statistics to get a first taste of how "Bayesianism" actually works, and also why it hasn't taken over the world already (hint: computational concerns).
I have used the following two books:
www.amazon.com/gp/aw/d/0470141158?pc_redir=1411138170&robot_redir=1
http://web4.cs.ucl.ac.uk/staff/d.barber/pmwiki/pmwiki.php?n=Brml.HomePage?from=Main.Textbook