Sigma Point (computational geometry)

In computational geometry, a sigma point is a representative sample chosen from a data distribution to accurately capture its characteristics, such as mean and covariance. Used in methods like the Unscented Kalman Filter, sigma points help approximate complex probabilistic functions by strategically selecting points that reflect the data's spread and shape. These points allow algorithms to estimate how uncertainty propagates through nonlinear systems efficiently, without requiring extensive calculations, thereby enabling more accurate and computationally feasible state estimation or data analysis.