👉 BD Math, or Bayesian Differential Geometry, is a mathematical framework that combines principles from differential geometry and Bayesian statistics to analyze geometric structures and their uncertainties. It extends traditional differential geometry by incorporating probabilistic reasoning, allowing for the modeling of geometric objects and their properties in a way that explicitly accounts for uncertainty. In BD Math, spaces are described using probability distributions over geometric parameters, enabling the quantification of geometric features such as curvature and distances with probabilistic confidence intervals. This approach is particularly useful in fields like computer vision, robotics, and machine learning, where understanding and quantifying uncertainty is crucial for making reliable predictions and decisions based on geometric data.
