We show that Skilling’s method of induction leads to a unique general theory of inductive inference, the method of Maximum relative Entropy (ME). The main tool for updating probabilities is the logarithmic relative entropy; other entropies such as those of Renyi or Tsallis are ruled out. We also show that Bayes updating is a special case of ME updating and thus, that the two are completely compatible.

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.