We use the method of Maximum (relative) Entropy to process information in the
form of observed data and moment constraints. The generic "canonical" form of
the posterior distribution for the problem of simultaneous updating with data
and moments is obtained. We discuss the general problem of non-commuting
constraints, when they should be processed sequentially and when
simultaneously. As an illustration, the multinomial example of die tosses is
solved in detail for two superficially similar but actually very different
problems.Comment: Presented at the 27th International Workshop on Bayesian Inference
and Maximum Entropy Methods in Science and Engineering, Saratoga Springs, NY,
July 8-13, 2007. 10 pages, 1 figure V2 has a small typo in the end of the
appendix that was fixed. aj=mj+1 is now aj=m(k-j)+
