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Measure Theory and Filtering

Measure Theory and Filtering
Author: Lakhdar Aggoun
Publisher: Cambridge University Press
Total Pages: 274
Release: 2004-09-13
Genre: Mathematics
ISBN: 9781139456241

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The estimation of noisily observed states from a sequence of data has traditionally incorporated ideas from Hilbert spaces and calculus-based probability theory. As conditional expectation is the key concept, the correct setting for filtering theory is that of a probability space. Graduate engineers, mathematicians and those working in quantitative finance wishing to use filtering techniques will find in the first half of this book an accessible introduction to measure theory, stochastic calculus, and stochastic processes, with particular emphasis on martingales and Brownian motion. Exercises are included. The book then provides an excellent users' guide to filtering: basic theory is followed by a thorough treatment of Kalman filtering, including recent results which extend the Kalman filter to provide parameter estimates. These ideas are then applied to problems arising in finance, genetics and population modelling in three separate chapters, making this a comprehensive resource for both practitioners and researchers.


Fundamentals of Stochastic Filtering

Fundamentals of Stochastic Filtering
Author: Alan Bain
Publisher: Springer Science & Business Media
Total Pages: 395
Release: 2008-10-08
Genre: Mathematics
ISBN: 0387768963

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This book provides 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 background to enable study of the recent literature. While no prior knowledge of stochastic filtering is required, readers are assumed to be familiar with measure theory, probability theory and the basics of stochastic processes. Most of the technical results that are required are stated and proved in the appendices. Exercises and solutions are included.


Measure Theory and its Applications

Measure Theory and its Applications
Author: J.M. Belley
Publisher: Springer
Total Pages: 335
Release: 2006-12-08
Genre: Mathematics
ISBN: 3540386904

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Stochastic Processes and Filtering Theory

Stochastic Processes and Filtering Theory
Author: Andrew H. Jazwinski
Publisher: Courier Corporation
Total Pages: 404
Release: 2013-04-15
Genre: Science
ISBN: 0486318192

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This unified treatment of linear and nonlinear filtering theory presents material previously available only in journals, and in terms accessible to engineering students. Its sole prerequisites are advanced calculus, the theory of ordinary differential equations, and matrix analysis. Although theory is emphasized, the text discusses numerous practical applications as well. Taking the state-space approach to filtering, this text models dynamical systems by finite-dimensional Markov processes, outputs of stochastic difference, and differential equations. Starting with background material on probability theory and stochastic processes, the author introduces and defines the problems of filtering, prediction, and smoothing. He presents the mathematical solutions to nonlinear filtering problems, and he specializes the nonlinear theory to linear problems. The final chapters deal with applications, addressing the development of approximate nonlinear filters, and presenting a critical analysis of their performance.


Stochastic Processes and Filtering Theory

Stochastic Processes and Filtering Theory
Author: Andrew H. Jazwinski
Publisher: Academic Press
Total Pages: 376
Release: 1970-01-31
Genre: Mathematics
ISBN: 0080960901

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This book presents a unified treatment of linear and nonlinear filtering theory for engineers, with sufficient emphasis on applications to enable the reader to use the theory. The need for this book is twofold. First, although linear estimation theory is relatively well known, it is largely scattered in the journal literature and has not been collected in a single source. Second, available literature on the continuous nonlinear theory is quite esoteric and controversial, and thus inaccessible to engineers uninitiated in measure theory and stochastic differential equations. Furthermore, it is not clear from the available literature whether the nonlinear theory can be applied to practical engineering problems. In attempting to fill the stated needs, the author has retained as much mathematical rigor as he felt was consistent with the prime objective—to explain the theory to engineers. Thus, the author has avoided measure theory in this book by using mean square convergence, on the premise that everyone knows how to average. As a result, the author only requires of the reader background in advanced calculus, theory of ordinary differential equations, and matrix analysis.


Quantum Measure Theory

Quantum Measure Theory
Author: J. Hamhalter
Publisher: Springer Science & Business Media
Total Pages: 412
Release: 2013-03-14
Genre: Mathematics
ISBN: 9401701199

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This book is the first systematic treatment of measures on projection lattices of von Neumann algebras. It presents significant recent results in this field. One part is inspired by the Generalized Gleason Theorem on extending measures on the projection lattices of von Neumann algebras to linear functionals. Applications of this principle to various problems in quantum physics are considered (hidden variable problem, Wigner type theorems, decoherence functional, etc.). Another part of the monograph deals with a fascinating interplay of algebraic properties of the projection lattice with the continuity of measures (the analysis of Jauch-Piron states, independence conditions in quantum field theory, etc.). These results have no direct analogy in the standard measure and probability theory. On the theoretical physics side, they are instrumental in recovering technical assumptions of the axiomatics of quantum theories only by considering algebraic properties of finitely additive measures (states) on quantum propositions.


Measure Theory

Measure Theory
Author: D. H. Fremlin
Publisher: Torres Fremlin
Total Pages: 292
Release: 2000
Genre: Fourier analysis
ISBN: 0953812952

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Stochastic Filtering Theory

Stochastic Filtering Theory
Author: G. Kallianpur
Publisher: Springer Science & Business Media
Total Pages: 326
Release: 2013-04-17
Genre: Science
ISBN: 1475765924

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This book is based on a seminar given at the University of California at Los Angeles in the Spring of 1975. The choice of topics reflects my interests at the time and the needs of the students taking the course. Initially the lectures were written up for publication in the Lecture Notes series. How ever, when I accepted Professor A. V. Balakrishnan's invitation to publish them in the Springer series on Applications of Mathematics it became necessary to alter the informal and often abridged style of the notes and to rewrite or expand much of the original manuscript so as to make the book as self-contained as possible. Even so, no attempt has been made to write a comprehensive treatise on filtering theory, and the book still follows the original plan of the lectures. While this book was in preparation, the two-volume English translation of the work by R. S. Liptser and A. N. Shiryaev has appeared in this series. The first volume and the present book have the same approach to the sub ject, viz. that of martingale theory. Liptser and Shiryaev go into greater detail in the discussion of statistical applications and also consider inter polation and extrapolation as well as filtering.


Fundamentals of Stochastic Filtering

Fundamentals of Stochastic Filtering
Author: Alan Bain
Publisher: Springer
Total Pages: 0
Release: 2008-11-01
Genre: Mathematics
ISBN: 9780387568546

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This book provides 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 background to enable study of the recent literature. While no prior knowledge of stochastic filtering is required, readers are assumed to be familiar with measure theory, probability theory and the basics of stochastic processes. Most of the technical results that are required are stated and proved in the appendices. Exercises and solutions are included.