Stochastic Differential Systems Analysis and Filtering PDF Download
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Author: V. S. Pugachev
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 584
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Book Description
Gives applied methods for studying stochastic differential systems--in particular, the methods for finding the finite-dimensional distributions of the state vector and of the output of such systems, and also the estimation methods of the state and of the parameters of differential systems based on observations (filtering and extrapolation theory). Also studied are stochastic differential equations of general type with arbitrary processes and independent increments. The equations with Wiener processes are considered as a special case. The construction of stochastic differential systems in the book is based on Pugachev's equations for finite-dimensional characteristic functions of the processes determined by stochastic differential equations. Includes end-of-chapter problems.
Author: Alan Bain
Publisher: Springer
ISBN: 9781441926425
Category : Mathematics
Languages : en
Pages : 0
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Book Description
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.
Author: Wendell H Fleming
Publisher: Springer
ISBN:
Category : Language Arts & Disciplines
Languages : en
Pages : 632
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Book Description
This volume has resulted from an IMA workshop that sought to provide a mix of topics from both traditional areas of stochastic control theory and newer areas of application. The papers represent a diversity of approaches and points of view and emphasize to different extents the underlying mathematical theory, or modeling issues or questions of computational implementation.
Author: Andrew H. Jazwinski
Publisher: Courier Corporation
ISBN: 0486318192
Category : Science
Languages : en
Pages : 404
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Book Description
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.
Author: Richard S. Bucy
Publisher: American Mathematical Soc.
ISBN: 9780821837825
Category : Mathematics
Languages : en
Pages : 240
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Book Description
This second edition preserves the original text of 1968, with clarification and added references. From the Preface to the Second Edition: ``Since the First Edition of this book, numerous important results have appeared--in particular stochastic integrals with respect to martingales, random fields, Riccati equation theory and realization of nonlinear filters, to name a few. In Appendix D, an attempt is made to provide some of the references that the authors have found useful and tocomment on the relation of the cited references to the field ... [W]e hope that this new edition will have the effect of hastening the day when the nonlinear filter will enjoy the same popularity in applications as the linear filter does now.''
Author: V. S. Pugachev
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 584
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Book Description
Gives applied methods for studying stochastic differential systems--in particular, the methods for finding the finite-dimensional distributions of the state vector and of the output of such systems, and also the estimation methods of the state and of the parameters of differential systems based on observations (filtering and extrapolation theory). Also studied are stochastic differential equations of general type with arbitrary processes and independent increments. The equations with Wiener processes are considered as a special case. The construction of stochastic differential systems in the book is based on Pugachev's equations for finite-dimensional characteristic functions of the processes determined by stochastic differential equations. Includes end-of-chapter problems.
Author: S. Watanabe
Publisher: Elsevier
ISBN: 008096012X
Category : Mathematics
Languages : en
Pages : 480
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Book Description
Stochastic Differential Equations and Diffusion Processes
Author: A. V. Balakrishnan
Publisher: Springer Science & Business Media
ISBN: 3642807593
Category : Computers
Languages : en
Pages : 260
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Book Description
This book is an outgrowth of a graduate course by the same title given at UCLA (System Science Department). presenting a Functional Analysis approach to Stochastic Filtering and Control Problems. As the writing progressed. several new points of view were developed and as a result the present work is more in the nature of a monograph on the subject than a distilled compendium of extant works. The subject of this volume is at the heart of the most used part of modern Control Theory - indeed. the bread-and-butter part. It includes the Linear (Bucy-Kalman) Filter Theory. the Feedback Control (regulation and trz.cking) Theory for plants with random disturbances. and Stochastic DifEerential Games. Linear Filter Theory is developed by a 3-Martingale approach and is perhaps the sleekest one to date. We hasten to add that although the terITlS are Engineering-oriented. and a background in Control Engineering is essential to understand the motiva tion. the work is totally mathematical. and in fact our aim is a rigorous mathematical presentation that is at once systematic. We begin with some preliminary necessary notions relating to Stochastic Processes. We follow Parthasarathy's work in inducing Wiener measure on the Banach Space of Continuous functions. We introduce the linear Stochastic integrals right away. We are then ready to treat linear Stochastic Differential Equations. We then look at the measures induced.
Author: George Yin
Publisher: Springer Nature
ISBN: 3030985199
Category : Mathematics
Languages : en
Pages : 466
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Book Description
This volume is a collection of research works to honor the late Professor Mark H.A. Davis, whose pioneering work in the areas of Stochastic Processes, Filtering, and Stochastic Optimization spans more than five decades. Invited authors include his dissertation advisor, past collaborators, colleagues, mentees, and graduate students of Professor Davis, as well as scholars who have worked in the above areas. Their contributions may expand upon topics in piecewise deterministic processes, pathwise stochastic calculus, martingale methods in stochastic optimization, filtering, mean-field games, time-inconsistency, as well as impulse, singular, risk-sensitive and robust stochastic control.
Author: Jie Xiong
Publisher: Oxford University Press
ISBN: 0199219702
Category : Business & Economics
Languages : en
Pages : 285
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Book Description
Stochastic Filtering Theory uses probability tools to estimate unobservable stochastic processes that arise in many applied fields including communication, target-tracking, and mathematical finance.As a topic, Stochastic Filtering Theory has progressed rapidly in recent years. For example, the (branching) particle system representation of the optimal filter has been extensively studied to seek more effective numerical approximations of the optimal filter; the stability of the filter with "incorrect" initial state, as well as the long-term behavior of the optimal filter, has attracted the attention of many researchers; and although still in its infancy, the study of singular filteringmodels has yielded exciting results.In this text, Jie Xiong introduces the reader to the basics of Stochastic Filtering Theory before covering these key recent advances. The text is written in a style suitable for graduates in mathematics and engineering with a background in basic probability.
Author: Michał Kisielewicz
Publisher: Springer
ISBN: 9781489989512
Category : Mathematics
Languages : en
Pages : 0
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Book Description
This book aims to further develop the theory of stochastic functional inclusions and their applications for describing the solutions of the initial and boundary value problems for partial differential inclusions. The self-contained volume is designed to introduce the reader in a systematic fashion, to new methods of the stochastic optimal control theory from the very beginning. The exposition contains detailed proofs and uses new and original methods to characterize the properties of stochastic functional inclusions that, up to the present time, have only been published recently by the author. The work is divided into seven chapters, with the first two acting as an introduction, containing selected material dealing with point- and set-valued stochastic processes, and the final two devoted to applications and optimal control problems. The book presents recent and pressing issues in stochastic processes, control, differential games, optimization and their application in finance, manufacturing, queueing networks, and climate control. Written by an award-winning author in the field of stochastic differential inclusions and their application to control theory, This book is intended for students and researchers in mathematics and applications; particularly those studying optimal control theory. It is also highly relevant for students of economics and engineering. The book can also be used as a reference on stochastic differential inclusions. Knowledge of select topics in analysis and probability theory are required.
