👉 Limited Difference (Ltd) Math is a probabilistic framework designed to model uncertainty in predictions, particularly useful in machine learning and statistics when dealing with noisy or incomplete data. Unlike traditional methods that often rely on point estimates, Ltd Math provides a distribution over predictions, offering both the mean and uncertainty (typically variance) of these estimates. This is achieved through a logarithmic transformation of the raw predictions, which stabilizes variance and makes the distribution more Gaussian-like. The core idea is to capture the inherent uncertainty in model predictions, allowing for more robust decision-making and confidence intervals. By framing predictions as distributions rather than single values, Ltd Math facilitates better risk assessment and more informed decisions in applications like regression, classification, and reinforcement learning.
