Absolutely the best book I've seen is: Christopher M. Bishop "Pattern Recognition and Machine Learning"
It's totally awesome! Strong points: - It have both Bayesian and non Bayesian ways explained - the explanation is clear - figures are so helpful (and aesthetic) - it concentrates on prediction and classification and have algorithmic perspective (contrary to MacKay's book) There is a free chapter on graphical models: http://research.microsoft.com/~cmbishop/PRML/Bishop-PRML-sample.pdf Lukasz Lew On 7/23/07, chrilly <[EMAIL PROTECTED]> wrote:
I have a Phd in statistics. But Bayesian methods were at that time a non-topic. I know the general principles, but I want to learn a little bit more about the latest developments in the field. Bayes is now chic, there are many books about it. I assume also a lot of bad ones. Can anyone recommend me a good state of the art book about Bayesian inference. Should be somewhat in the applied direction, but also with a sound mathematical background. Chrilly _______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
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