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Take independent and identically distributed draws from the uniform three-outcome reference above. An observed sample mean at least $1/2$ is unusual because the source expected value is zero. For two draws, the only successful ordered pairs are $(0,1),(1,0),(1,1)$, giving probability $3/9=1/3$.

For long samples, the relevant empirical distributions have mean at least $1/2$. The method of types assigns a profile $q$ a leading probability cost $e^{-nD(q\Vert p)}$. The least costly admissible profile is the information projection on three outcomes: in this example the minimizing mean is on the boundary $1/2$, and the leading exponent is approximately $0.197378$ nats per draw. The exponential scale is not an exact finite-sample probability formula.^[ref]

The attraction of this connection is that a constrained optimization problem predicts the most probable way an unusual aggregate observation can occur. It links relative entropy, expected value constraints and rare empirical distributions without identifying a rare event with just one exact attainable type class.

Let the possible numerical outcomes be $-1,0,1$, and give the reference law $p$ uniform distribution on a finite set: $p=(1/3,1/3,1/3)$. We want a law $q$ with expected value $1/2$. Write the three probabilities in outcome order as $(q_{-},q_0,q_{+})$. The mean constraint is $q_{+}-q_{-}=1/2$, while normalization requires $q_{-}+q_0+q_{+}=1$.

These conditions leave a whole line of possible probability distributions:For example, $(0,1/2,1/2)$ and $(1/4,0,3/4)$ both have the required mean. The constraint alone does not choose between them. Information projection selects the point on this line with the smallest relative entropy from the reference.

Exponential tilting gives weights proportional to $e^{-\theta },1,e^{\theta }$. Set $t=e^{\theta }>0$ and multiply these weights by $t$ to obtain $q^{\ast }=(1,t,t^2)/(1+t+t^2)$. Enforcing the mean givesThe positive root is $t=(1+\sqrt{13})/2$, henceThe most favored outcome becomes $1$, but all three remain possible. This is an exact constructed example with a stipulated reference law and constraint, not a fitted distribution from measurements.