Prior Knowledge Accelerate Variance Computing

This paper introduces Prior Knowledge Acceleration (PKA), a method to speed up variance calculations by leveraging prior knowledge of the original dataset's variance. PKA enables the efficient updating of variance when adding new data, reducing computational costs by avoiding full recalculations. We derive expressions for both population and sample variance using PKA and compare it to Sheldon M. Ross's method. Stimulated results show that PKA can reduce calculation time by up to 75.6\%, especially when the original dataset is large. PKA offers a promising approach for accelerating variance computations in large-scale data analysis, though its effectiveness depends on assumptions of constant computational time.