ISBN: 0387768955
[SR: 1388678], Hardcover, [EAN: 9780387768953], Springer, Springer, Book, [PU: Springer], Springer, The objective of stochastic filtering is to determine the best estimate for the state of a stochastic dynamical system from partial observations. The solution of this problem in the linear case is the well known Kalman-Bucy filter which has found widespread practical application. The purpose of this book is to provide 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. The book is intended as a reference for graduate students and researchers..., 268091, Accounting, 659892, Audits & Auditing, 659894, Book-keeping, 268092, Cost, 268093, Financial, 268094, Financial Reporting & Statements, 268097, International, 268098, Management Accounting, 68, Business, Finance & Law, 1025612, Subjects, 266239, Books, 268179, Professional Finance, 659892, Audits & Auditing, 268180, Banking, 659982, Budgeting, 268181, Corporate, 659984, Forecasting, 659986, Foreign Exchange, 268183, Insurance, 659992, International Finance, 268194, Investments & Securities, 268216, Public, 659994, Purchasing & Procurement, 659996, Risk Management, 659998, Taxation, 660000, Venture Capital, 68, Business, Finance & Law, 1025612, Subjects, 266239, Books, 278166, Automatic Control, 278160, Electronics Engineering, 278141, Electronics & Communications Engineering, 278115, Engineering & Technology, 57, Science & Nature, 1025612, Subjects, 266239, Books, 922314, Production, Manufacturing & Operational, 922402, Computer Aided Manufacture, 922322, Health & Safety, 278291, Industrial Design, 922320, Manufacturing, 922328, Materials & Industries, 922358, Productivity, 278168, Robotics, 278115, Engineering & Technology, 57, Science & Nature, 1025612, Subjects, 266239, Books, 278329, Applied Mathematics, 922530, Mathematical Modelling, 278335, Mathematics for Scientists & Engineers, 278419, Physics, 278320, Mathematics, 57, Science & Nature, 1025612, Subjects, 266239, Books, 278373, Numbers, 278320, Mathematics, 57, Science & Nature, 1025612, Subjects, 266239, Books, 278385, Probability & Statistics, 278320, Mathematics, 57, Science & Nature, 1025612, Subjects, 266239, Books, 922942, Maths, 922868, Popular Science, 57, Science & Nature, 1025612, Subjects, 266239, Books, 571046, Electronics & Telecommunications Engineering, 564346, Engineering, 564334, Scientific, Technical & Medical, 1025612, Subjects, 266239, Books, 570878, Statistics & Probability, 570874, Applied Mathematics, 564352, Mathematics, 564334, Scientific, Technical & Medical, 1025612, Subjects, 266239, Books
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Alan Bain, Dan Crisan:
Fundamentals of Stochastic Filtering (Stochastic Modelling and Applied Probability) - hardcoverISBN: 0387768955
[SR: 1388678], Hardcover, [EAN: 9780387768953], Springer, Springer, Book, [PU: Springer], Springer, The objective of stochastic filtering is to determine the best estimate for the state of a stochastic dynamical system from partial observations. The solution of this problem in the linear case is the well known Kalman-Bucy filter which has found widespread practical application. The purpose of this book is to provide 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. The book is intended as a reference for graduate students and researchers..., 268091, Accounting, 659892, Audits & Auditing, 659894, Book-keeping, 268092, Cost, 268093, Financial, 268094, Financial Reporting & Statements, 268097, International, 268098, Management Accounting, 68, Business, Finance & Law, 1025612, Subjects, 266239, Books, 268179, Professional Finance, 659892, Audits & Auditing, 268180, Banking, 659982, Budgeting, 268181, Corporate, 659984, Forecasting, 659986, Foreign Exchange, 268183, Insurance, 659992, International Finance, 268194, Investments & Securities, 268216, Public, 659994, Purchasing & Procurement, 659996, Risk Management, 659998, Taxation, 660000, Venture Capital, 68, Business, Finance & Law, 1025612, Subjects, 266239, Books, 278166, Automatic Control, 278160, Electronics Engineering, 278141, Electronics & Communications Engineering, 278115, Engineering & Technology, 57, Science & Nature, 1025612, Subjects, 266239, Books, 922314, Production, Manufacturing & Operational, 922402, Computer Aided Manufacture, 922322, Health & Safety, 278291, Industrial Design, 922320, Manufacturing, 922328, Materials & Industries, 922358, Productivity, 278168, Robotics, 278115, Engineering & Technology, 57, Science & Nature, 1025612, Subjects, 266239, Books, 278329, Applied Mathematics, 922530, Mathematical Modelling, 278335, Mathematics for Scientists & Engineers, 278419, Physics, 278320, Mathematics, 57, Science & Nature, 1025612, Subjects, 266239, Books, 278373, Numbers, 278320, Mathematics, 57, Science & Nature, 1025612, Subjects, 266239, Books, 278385, Probability & Statistics, 278320, Mathematics, 57, Science & Nature, 1025612, Subjects, 266239, Books, 922942, Maths, 922868, Popular Science, 57, Science & Nature, 1025612, Subjects, 266239, Books, 571046, Electronics & Telecommunications Engineering, 564346, Engineering, 564334, Scientific, Technical & Medical, 1025612, Subjects, 266239, Books, 570878, Statistics & Probability, 570874, Applied Mathematics, 564352, Mathematics, 564334, Scientific, Technical & Medical, 1025612, Subjects, 266239, Books
Amazon.co.uk |
ISBN: 9780387768953
ID: 9780387768953
The objective of shastic filtering is to determine the best estimate for the state of a shastic dynamical system from partial observations. The solution of this problem in the linear case is the well known Kalman-Bucy filter which has found widespread practical application. The purpose of this book is to provide a rigorous mathematical treatment of the non-linear shastic filtering problem using modern methods. Particular emphasis is placed on the theoretical analysis of numerical methods for The objective of shastic filtering is to determine the best estimate for the state of a shastic dynamical system from partial observations. The solution of this problem in the linear case is the well known Kalman-Bucy filter which has found widespread practical application. The purpose of this book is to provide a rigorous mathematical treatment of the non-linear shastic 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 shastic filtering is required, readers are assumed to be familiar with measure theory, probability theory and the basics of shastic processes. Most of the technical results that are required are stated and proved in the appendices. The book is intended as a reference for graduate students and researchers interested in the field. It is also suitable for use as a text for a graduate level course on shastic filtering. Suitable exercises and solutions are included. Textbooks New, Books~~Mathematics~~Applied, Fundamentals-of-Stochastic-Filtering~~Alan-Bain, 999999999, Fundamentals of Stochastic Filtering, Alan Bain, Dan Crisan, 0387768955, Springer New York, , , , , Springer New York
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2008, ISBN: 9780387768953
[ED: Buch], [PU: Springer-Verlag GmbH], Neuware - The objective of stochastic filtering is to determine the best estimate for the state of a stochastic dynamical system from partial observations. The solution of this problem in the linear case is the well known Kalman-Bucy filter which has found widespread practical application. The purpose of this book is to provide 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. The book is intended as a reference for graduate students and researchers interested in the field. It is also suitable for use as a text for a graduate level course on stochastic filtering (suitable exercises and solutions are included)., [SC: 0.00]
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2008, ISBN: 9780387768953
[ED: Buch], [PU: Springer-Verlag GmbH], Neuware - The objective of stochastic filtering is to determine the best estimate for the state of a stochastic dynamical system from partial observations. The solution of this problem in the linear case is the well known Kalman-Bucy filter which has found widespread practical application. The purpose of this book is to provide 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. The book is intended as a reference for graduate students and researchers interested in the field. It is also suitable for use as a text for a graduate level course on stochastic filtering (suitable exercises and solutions are included)., [SC: 0.00]
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Title: | Fundamentals of Stochastic Filtering |
ISBN: | 0387768955 |
Details of the book - Fundamentals of Stochastic Filtering
EAN (ISBN-13): 9780387768953
ISBN (ISBN-10): 0387768955
Hardcover
Publishing year: 2008
Publisher: Springer-Verlag GmbH
390 Pages
Weight: 0,680 kg
Language: eng/Englisch
Book in our database since 25.01.2008 03:20:11
Book found last time on 28.06.2015 20:59:30
ISBN/EAN: 0387768955
ISBN - alternate spelling:
0-387-76895-5, 978-0-387-76895-3
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