Affinely Parametrized State-space Models: Ways to Maximize the Likelihood Function
Affinely Parametrized State-space Models: Ways to Maximize the Likelihood Function is a scholarly work, published in 2018 in ''IFAC Proceedings Volumes''. The main subjects of the publication include estimation theory, simple, space, rank, affine transformation, focus, biological function, state space, state, mathematical optimization, expectation–maximization algorithm, computer science, maximization, prime, system identification, graph dimension, likelihood function, uncertainty quantification, fault detection and isolation, applied mathematics, mathematics, and algorithm. <p>Using Maximum Likelihood (or Prediction Error) methods to identify linear state space model is a prime technique.