Uniform-reference information projection maximizes entropy ($D(q\Vert u)=\ln m-H_e(q)$)

For a uniform distribution on a finite set of $m$ outcomes, direct expansion of relative entropy giveswhere $H_e$ is information entropy in nats. Therefore minimizing relative entropy from this particular reference is equivalent to maximizing information entropy subject to the same constraint. This statement depends on the uniform reference; an arbitrary reference contributes its own weights.

In the three-outcome example, $H_e(q^{\ast })=\ln 3-D(q^{\ast }\Vert p)\approx 0.901235$ nats. The mean fixes one aggregate quantity, and information projection chooses how to distribute the remaining uncertainty. This extends maximum entropy on a finite alphabet, whose unconstrained maximizer is the uniform law itself.