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100   $a20080128d2008      uy             0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 13$aAn introduction to stochastic filtering theory$b[electronic resource] /$fJie Xiong
210   $aOxford ;$aNew York $cOxford University Press$d2008
215   $a1 online resource (285 p.)
225 1 $aOxford graduate texts in mathematics ;$v18
300   $aDescription based upon print version of record.
311   $a0-19-921970-2 
320   $aIncludes bibliographical references (p. [255]-265) and index.
327   $aContents; 1 Introduction; 2 Brownian motion and martingales; 3 Stochastic integrals and Ito?'s formula; 4 Stochastic differential equations; 5 Filtering model and Kallianpur-Striebel formula; 6 Uniqueness of the solution for Zakai's equation; 7 Uniqueness of the solution for the filtering equation; 8 Numerical methods; 9 Linear filtering; 10 Stability of non-linear filtering; 11 Singular filtering; Bibliography; List of Notations; Index
330   $aStochastic filtering theory is a field that has seen a rapid development in recent years and this book, aimed at graduates and researchers in applied mathematics, provides an accessible introduction covering recent developments. - ;Stochastic Filtering Theory uses probability tools to estimate unobservable stochastic processes that arise in many applied fields including communication, target-tracking, and mathematical finance. As a topic, Stochastic Filtering Theory has progressed rapidly in recent years. For example, the (branching) particle system representation of the optimal filter has bee
410  0$aOxford graduate texts in mathematics ;$v18.
606   $aStochastic processes
606   $aFilters (Mathematics)
606   $aPrediction theory
615  0$aStochastic processes.
615  0$aFilters (Mathematics)
615  0$aPrediction theory.
676   $a519.2/3
700   $aXiong$b Jie$0736517
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910825354503321
996   $aAn introduction to stochastic filtering theory$93961288
997   $aUNINA