Maximum Entropy Method

The Maximum Entropy Method is a technique used to make the best possible inference from incomplete or uncertain data by choosing the solution that has the greatest amount of uncertainty, or "entropy," among all that fit the known information. This approach ensures no unwarranted assumptions are added, resulting in a balanced, objective estimate. It's often used in areas like signal processing and image reconstruction to recover missing information in a way that is most consistent with what is known, while avoiding overfitting or introducing bias. Essentially, it favors the most unbiased, least assumptive solution that aligns with the available data.