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010   $a1-281-32617-8
010   $a9786611326173
010   $a1-4200-2867-7
024 7 $a10.1201/9781420028676
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035   $a(EBL)263751
035   $a(OCoLC)475983909
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035   $a(PQKBTitleCode)TC0000186834
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100   $a20180331d2006      uy             0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 12$aA Kalman filter primer /$fR.L. Eubank
210  1$aBoca Raton, Fla. :$cChapman & Hall/CRC,$d2006.
215   $a1 online resource (199 p.)
225 1 $aStatistics, textbooks and monographs ;$vv. 186
300   $aDescription based upon print version of record.
311   $a0-8247-2365-1 
320   $aIncludes bibliographical references (p. 183-184) and index.
327   $achapter 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.
330   $aEubank (mathematics and statistics, Arizona State U.) offers a self-contained, concise rigorous derivation of all the basic Kalman filter recursions from first principles. He lays out the basic prediction problem for signal-plus-noise models, deriving the Gramm-Schmidt algorithm and Cholesky decomposition. He covers the fundamental covariance struc
410  0$aStatistics, textbooks and monographs ;$vv. 186.
606   $aKalman filtering$vTextbooks
615  0$aKalman filtering
676   $a519.2/3
700   $aEubank$b R. L$g(Randy L.),$01586671
801  0$bFlBoTFG
801  1$bFlBoTFG
906   $aBOOK
912   $a9910799980603321
996   $aA Kalman filter primer$93873399
997   $aUNINA