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My interest in Bayesian decision theory stem from my need to apply Bayesian analysis to the pattern analysis task I'm undertaking lately. I encountered a chapter titled "Bayesian Decision Theory" as I was reading the book "Pattern Classification" by R. Duda, et al.

I went through the chapter back and forth for a few times. Yet I still didn't get a grasp what the essence of the theory of "Baysian decision" is. I googled the Internet for the term and with the exception of a few good references, not many of them shed new insight.

Finally I discovered the sample pages of James O. Berger's book "Statistical Decision Theory and Bayesian Analysis" in google book search.

• There's no such thing as the so called null hypothesis in the overly simplified sense, i.e. nothing is perfect: coins are always unfair.
• Making inferences without a context of decision making is a waste of effort, since the inference always contains less information than the data itself.