Fundamentals of Stochastic Filtering
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Fundamentals of Stochastic Filtering by Alan Bain
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.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)
Dr. Dan Crisan is a Reader in Mathematics at Imperial College London, and his research interests include stochastic analysis and its applications in engineering and finance. Stochastic filtering theory, which deals with the estimate of partially seen signals, is his main area of interest. Signal processing, satellite tracking, global positioning systems, spell checks, weather forecasting, EEG/ECG analysis, and computer vision are just a few of the many uses of stochastic filtering. Basics of Stochastic Filtering was published by Springer in 2009. Dr. Crisan is a member of the Journal of Mathematics and Computation's editorial board.
He's also a teacher with a lot of experience. At Imperial College, he taught stochastic filtering, numerical stochastics, and measure-valued processes, as well as applied probability and stochastic calculus and applications. At Cambridge University, he taught stochastic calculus and applications.
| SKU | Unavailable |
| ISBN 13 | 9780387768953 |
| ISBN 10 | 0387768955 |
| Title | Fundamentals of Stochastic Filtering |
| Author | Alan Bain |
| Series | Stochastic Modelling And Applied Probability |
| Condition | Unavailable |
| Publisher | Springer-Verlag New York Inc. |
| Year published | 2008-10-23 |
| Number of pages | 390 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |
