02653nam 22007214a 450 991096105050332120200520144314.097866113261739781040199923104019992597804291175960429117590978128132617112813261789781420028676142002867710.1201/9781420028676(CKB)1000000000346551(EBL)263751(OCoLC)475983909(SSID)ssj0000186834(PQKBManifestationID)11166774(PQKBTitleCode)TC0000186834(PQKBWorkID)10252943(PQKB)11166685(Au-PeEL)EBL263751(CaPaEBR)ebr10143621(CaONFJC)MIL132617(OCoLC)437168728(OCoLC)123439160(OCoLC)61253979(FINmELB)ELB154325(MiAaPQ)EBC263751(EXLCZ)99100000000034655120050805d2006 uy 0engur|n|---|||||txtccrA Kalman filter primer /R.L. Eubank1st ed.Boca Raton, Fla. Chapman & Hall/CRC20061 online resource (199 p.)Statistics, textbooks and monographs ;v. 186Description based upon print version of record.9780824723651 0824723651 Includes bibliographical references (p. 183-184) and index.chapter 1 Signal-Plus-Noise Models -- chapter 2 The Fundamental Covariance Structure -- chapter 3 Recursions for L and L−1 -- chapter 4 Forward Recursions -- chapter 5 Smoothing -- chapter 6 Initialization -- chapter 7 Normal Priors -- chapter 8 A General State-Space Model.Eubank (mathematics and statistics, Arizona State U.) offers a self-contained, concise rigorous derivation of all the basic Kalman filter recursions from first principles. He lays out the basic prediction problem for signal-plus-noise models, deriving the Gramm-Schmidt algorithm and Cholesky decomposition. He covers the fundamental covariance strucStatistics, textbooks and monographs ;v. 186.Kalman filteringTextbooksKalman filtering519.2/3Eubank Randall L.1952-102811MiAaPQMiAaPQMiAaPQBOOK9910961050503321Kalman filter primer715154UNINA