Abstract
Abstract Some applications of the Wiener-Kolmogorov and Bayes approaches are discussed with regard to the construction of recursive equations for the best linear estimator of a stochastic parameter. Solutions are given for the prediction, filtering and smoothing cases when the parameter follows a vector Markov process and is observed with error. The Bayes solution is used to generate a canonical factorization of the autocovariance generating function of the observed series. Generalization of these techniques to other models is also discussed.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.