In the Boltzmann way of thinking about entropy, the knowledge of which macrostate we are in tells us less and less about the microstate as entropy increases; the Gibbs approach inverts this perspective and defines entropy in terms of how much we know. Instead of starting with a coarse-graining on the space of states, we start with a probability distribution: the percentage chance, for each possible microstate, that the system is actually in that microstate right now. Then Gibbs gives us a formula, analogous to Boltzmann’s, for calculating the entropy associated with that probability
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