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.
Reviews

From the reviews:

"This book provides a rigorous mathematical treatment of the nonlinear stochastic filtering problem with particular emphasis on numerical methods. ... The text is essentially self-contained ... . In an appendice the required results from measure theory and stochastic analysis are stated and proved. Intended readers are researchers and graduate students that have an interest in theoretical aspects of stochastic filtering. The text is supplemented with many exercises and detailed solutions. ... a standard reference for teaching and working in the field of stochastic filtering." (H. M. Mai, Zentralblatt MATH, Vol. 1176, 2010)

"This book is one of the few books dealing with both the theoretical foundations and modern stochastic particle techniques in stochastic filtering through the entire text. ... I highly recommend this book to any researcher in applied mathematics, as well as to any researchers in engineering and computer sciences with some background in statistics and probability. ... The book can also serve as a useful text for an informal seminar or a second year graduate course on stochastic filtering." (Pierre Del Moral, Bulletin of the American Mathematical Society, Vol. 48 (2), April, 2011)

Book Information
ISBN 9780387768953
Author Alan Bain
Format Hardback
Page Count 390
Imprint Springer-Verlag New York Inc.
Publisher Springer-Verlag New York Inc.