Entropy is a measure of the number of possible states a system can be in.
The principle of maximum entropy aims to define a constraint for each piece of information observed and to choose the distribution which maximizes the entropy while remaining consistent with respect to all of these constraints.
The principle of maximum entropy is also a model building rule that requires selecting the most unpredictable a priori hypothesis called maximum entropy if only one parameter is known on a probability distribution. The goal is to maximize uniformity or uncertainty when assuming a prior probability so that subjective bias is minimized in model results.
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