Maximum Entropy Modeling

Maximum Entropy Modeling is a statistical method used to predict outcomes based on incomplete information. It selects the most probable distribution that fits known data without assuming any additional, unverified details—meaning it chooses the "least biased" model consistent with what we do know. This approach ensures the model is as simple as possible while accurately reflecting the information available. It's commonly used in language processing, image recognition, and other areas where making fair, data-driven predictions is essential, prioritizing neutrality and avoiding unwarranted assumptions.