Fundamentals of stochastic filtering /

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Bibliographic Details
Author / Creator:Bain, Alan.
Imprint:New York ; London : Springer, 2009.
Description:1 online resource (xiii, 390 p.)
Language:English
Series:Stochastic modelling and applied probability ; 60
Stochastic modelling and applied probability ; 60.
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11957424
Hidden Bibliographic Details
Other authors / contributors:Crisan, Dan.
ISBN:9780387768960
0387768963
9780387768953
0387768955
Notes:Includes bibliographical references (p. [367]-382) and indexes.
Summary:The objective of stochastic filtering is to determine the best estimate for the state of a stochastic dynamical system from partial observations. The solution of this problem in the linear case is the well known Kalman-Bucy filter which has found widespread practical application. The purpose of this book is to provide a rigorous mathematical treatment of the non-linear stochastic filtering problem using modern methods. Particular emphasis is placed on the theoretical analysis of numerical methods for the solution of the filtering problem via particle methods. The book should provide sufficient
Other form:Print version: Bain, Alan. Fundamentals of stochastic filtering. New York : Springer, c2009 9780387768953
Description
Summary:Many aspects of phenomena critical to our lives can not be measured directly. Fortunately models of these phenomena, together with more limited obs- vations frequently allow us to make reasonable inferences about the state of the systems that a?ect us. The process of using partial observations and a stochastic model to make inferences about an evolving system is known as stochastic ?ltering. The objective of this text is to assist anyone who would like to become familiar with the theory of stochastic ?ltering, whether graduate student or more experienced scientist. The majority of the fundamental results of the subject are presented using modern methods making them readily available for reference. The book may also be of interest to practitioners of stochastic ?ltering, who wish to gain a better understanding of the underlying theory. Stochastic ?ltering in continuous time relies heavily on measure theory, stochasticprocessesandstochasticcalculus.Whileknowledgeofbasicmeasure theory and probability is assumed, the text is largely self-contained in that the majority of the results needed are stated in two appendices. This should make it easy for the book to be used as a graduate teaching text. With this in mind, each chapter contains a number of exercises, with solutions detailed at the end of the chapter.
Physical Description:1 online resource (xiii, 390 p.)
Bibliography:Includes bibliographical references (p. [367]-382) and indexes.
ISBN:9780387768960
0387768963
9780387768953
0387768955