Relative entropy measures the expected log-likelihood ratio and vanishes only when the distributions agree. I will separate the object being defined from the consequence being claimed.
The mathematical object
Continuous-time processes are described by finite-dimensional distributions plus path regularity. A Poisson process \(N_t\) has independent increments, while Brownian motion \(B_t\) has Gaussian increments.
The first display fixes the mathematical data. I label it \(\mathsf{data}\) mentally, while the next is the \(\mathsf{claim}\); the bridge between them is the displayed \(\Longrightarrow\), not an automatic implication.
One explicit computation
Now evaluate one representative case. The result should agree with the structural law above, but it is obtained without assuming the conclusion.
Why the identity matters
The aligned summary deliberately puts the datum and conclusion on different rows. Mathematically, this is the distinction between specifying an object and proving a property of it.
Where it can fail
Matching means and variances does not identify a distribution. Independence, increment laws, and sample-path properties are separate ingredients.
A symbolic summary is trustworthy only because the example and limitation remain visible beside it. The box compresses the conclusion without hiding its origin.