| Fundamentals of Stochastic Filtering 2009 Edition Contributor(s): Bain, Alan (Author), Crisan, Dan (Author) |
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ISBN: 0387768955 ISBN-13: 9780387768953 Publisher: Springer OUR PRICE: $161.49 Product Type: Hardcover - Other Formats Published: October 2008 Annotation: The purpose of this book is to provide a modern, solid and accessible starting point in studying stochastic filtering. The book is structured in two parts: the first part deals with the theoretical aspects of the problem of stochastic filtering, whilst the second part looks at various numerical methods to solve the filtering problem, with the main emphasis on the class of particle approximations. The focus of the stochastic filtering is on estimating an evolving dynamical system, the signal, customarily modelled by a stochastic process. Former description of the process is utilized to make full use of the richness of the tools supplied by stochastic calculus. Some of the topics this book adresses are: basic concept of conditional expectation, filtering problem, uniqueness of the solution of the filtering equations, and finite-dimensional filters. |
| Additional Information |
| BISAC Categories: - Mathematics | Probability & Statistics - General - Mathematics | Applied |
| Dewey: 519.22 |
| LCCN: 2008938477 |
| Series: Stochastic Modelling and Applied Probability |
| Physical Information: 1" H x 9.3" W x 6.3" (1.50 lbs) 408 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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. |