  Jaynes invented the Brandeis Dice Problem as a simple illustration of the
MaxEnt (Maximum Entropy) procedure that he had demonstrated to work so well in
Statistical Mechanics. I construct here two alternative solutions to his toy
problem. One, like Jaynes' solution, uses MaxEnt and yields an analogue of the
canonical ensemble, but at a different level of description. The other uses
Bayesian updating and yields an analogue of the micro-canonical ensemble. Both,
unlike Jaynes' solution, yield error bars, whose operational merits I discuss.
These two alternative solutions are not equivalent for the original Brandeis
Dice Problem, but become so in what must, therefore, count as the analogue of
the thermodynamic limit, $M$-sided dice with $M\rightarrow\infty$. Whereas the
mathematical analogies between the dice problem and Stat Mech are quite close,
there are physical properties that the former lacks but that are crucial to the
workings of the latter. Stat Mech is more than just MaxEnt.
