Jaynes Principle of Maximum Entropy

The Principle of Maximum Entropy, developed by E.T. Jaynes, suggests that when making predictions based on incomplete information, you should choose the probability distribution that has the highest entropy—a measure of uncertainty—while still matching available data. This approach ensures that no unwarranted assumptions are introduced, allowing for the most unbiased estimate given what is known. Essentially, it helps select the most objective and least biased model consistent with known constraints, providing a rational way to infer probabilities in uncertain situations.