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 rea… Mehr…
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.; PDF; Scientific, Technical and Medical > Mathematics > Probability & statistics, Springer New York<
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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 re… Mehr…
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. Books > Mathematics eBook, Springer Shop<
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new in stock. Versandkosten:zzgl. Versandkosten. Details...
(*) Derzeit vergriffen bedeutet, dass dieser Titel momentan auf keiner der angeschlossenen Plattform verfügbar ist.
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 rea… Mehr…
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.; PDF; Scientific, Technical and Medical > Mathematics > Probability & statistics, Springer New York<
No. 9780387768960. Versandkosten:Instock, Despatched same working day before 3pm, zzgl. Versandkosten.
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 re… Mehr…
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. Books > Mathematics eBook, Springer Shop<
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Detailangaben zum Buch - Fundamentals of Stochastic Filtering
EAN (ISBN-13): 9780387768960 Erscheinungsjahr: 2008 Herausgeber: Springer New York 390 Seiten Sprache: eng/Englisch
Buch in der Datenbank seit 2009-09-15T18:22:11+02:00 (Vienna) Detailseite zuletzt geändert am 2024-02-22T13:32:10+01:00 (Vienna) ISBN/EAN: 9780387768960
ISBN - alternative Schreibweisen: 978-0-387-76896-0 Alternative Schreibweisen und verwandte Suchbegriffe: Autor des Buches: crisan, bain, holder nancy Titel des Buches: fundamentals, stochastic
Daten vom Verlag:
Autor/in: Alan Bain; Dan Crisan Titel: Stochastic Modelling and Applied Probability; Fundamentals of Stochastic Filtering Verlag: Springer; Springer US 390 Seiten Erscheinungsjahr: 2008-10-08 New York; NY; US Sprache: Englisch 106,99 € (DE) 110,00 € (AT) 130,00 CHF (CH) Available XIII, 390 p.
EA; E107; eBook; Nonbooks, PBS / Mathematik/Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik; Wahrscheinlichkeitsrechnung und Statistik; Verstehen; Filtering; Fundamentals; Modeling; Probability theory; Stochastic; Stochastic Processes; algorithms; calculus; filtering problem; measure theory; stochastic process; quantitative finance; B; Probability Theory; Control, Robotics, Automation; Numerical Analysis; Mathematics in Business, Economics and Finance; Mathematics and Statistics; Stochastik; Regelungstechnik; Numerische Mathematik; Angewandte Mathematik; Wirtschaftswissenschaft, Finanzen, Betriebswirtschaft und Management; BB
Filtering Theory.- The Stochastic Process ?.- The Filtering Equations.- Uniqueness of the Solution to the Zakai and the Kushner–Stratonovich Equations.- The Robust Representation Formula.- Finite-Dimensional Filters.- The Density of the Conditional Distribution of the Signal.- Numerical Algorithms.- Numerical Methods for Solving the Filtering Problem.- A Continuous Time Particle Filter.- Particle Filters in Discrete Time. The authors are an authority in the stochastic filtering field An assortment of Measure Theory, Probability Theory and Stochastic Analysis results are included in order to make this book as self contained as possible Exercises and solutions included throughout;
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