filtreerimismeetod
Filtreerimismeetod, also known as filtering methods, refers to a class of algorithms used in machine learning and signal processing to estimate the state of a dynamic system from a series of noisy measurements. These methods are essential when dealing with systems that evolve over time and are subject to uncertainty. The core idea is to recursively update an estimate of the system's state by combining predictions based on a mathematical model of the system with new incoming measurements.
The process typically involves two main steps: prediction and update. In the prediction step, the algorithm
A prominent example of a filtreerimismeetod is the Kalman filter. The Kalman filter is optimal for linear