Principles of Nonlinear Filtering Theory (Algorithms and Computation in Mathematics, 33)

Yau, Stephen S.-T.; Chen, Xiuqiong; Jiao, Xiaopei; Kang, Jiayi; Sun, Zeju; Tao, Yangtianze

ISBN 10: 3031776836 ISBN 13: 9783031776830
Editorial: Springer, 2024
Nuevos Encuadernación de tapa dura

Librería: Ria Christie Collections, Uxbridge, Reino Unido Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Vendedor de AbeBooks desde 25 de marzo de 2015

Este artículo en concreto ya no está disponible.

Descripción

Descripción:

In. N° de ref. del artículo ria9783031776830_new

Denunciar este artículo

Sinopsis:

This text presents a comprehensive and unified treatment of nonlinear filtering theory, with a strong emphasis on its mathematical underpinnings. It is tailored to meet the needs of a diverse readership, including mathematically inclined engineers and scientists at both graduate and post-graduate levels. What sets this book apart from other treatments of the topic is twofold. Firstly, it offers a complete treatment of filtering theory, providing readers with a thorough understanding of the subject. Secondly, it introduces updated methodologies and applications that are crucial in today’s landscape. These include finite-dimensional filters, the Yau-Yau algorithm, direct methods, and the integration of deep learning with filtering problems. The book will be an invaluable resource for researchers and practitioners for years to come.

With a rich historical backdrop dating back to Gauss and Wiener, the exposition delves into the fundamental principles underpinning the estimation of stochastic processes amidst noisy observations-a critical tool in various applied domains such as aircraft navigation, solar mapping, and orbit determination, to name just a few. Substantive exercises and examples given in each chapter provide the reader with opportunities to appreciate applications and ample ways to test their understanding of the topics covered. An especially nice feature for those studying the subject independent of a traditional course setting is the inclusion of solutions to exercises at the end of the book.

The book is structured into three cohesive parts, each designed to build the reader’s understanding of nonlinear filtering theory.  In the first part, foundational concepts from probability theory, stochastic processes, stochastic differential equations, and optimization are introduced, providing readers with the necessary mathematical background. The second part delves into theoretical aspects of filtering theory, covering topics such as the stochastic partial differential equation governing the posterior density function of the state, and the estimation algebra theory of systems with finite-dimensional filters. Moving forward, the third part of the book explores numerical algorithms for solving filtering problems, including the Yau-Yau algorithm, direct methods, classical filtering algorithms like the particle filter, and the intersection of filtering theory with deep learning.

Acerca del autor:

Stephen Shing-Toung Yau (Life Fellow, IEEE) received the Ph.D. degree in mathematics from the State University of New York, Stony Brook, NY, USA, in 1976. He was a Member of the Institute of Advanced Study, Princeton, NJ, USA, from 1976 to 1977 and 1981 to 1982. He was a Benjamin Pierce Assistant Professor with Harvard University, Cambridge, MA, USA, from 1977 to 1980. He then joined the Department of Mathematics, Statistics and Computer Science (MSCS), University of Illinois at Chicago (UIC), Chicago, IL, USA, and served for more than 30 years. From 2005 to 2011, he was a Joint Professor with the Department of Electrical and Computer Engineering, MSCS, UIC. After retiring in 2011, he joined the Department of Mathematical Sciences at Tsinghua University in Beijing, China, where he served for over 10 years. In 2022, he became a research fellow at the Beijing Institute of Mathematical Sciences and Applications (BIMSA) in Beijing, China, to begin his new research.  His research interests include nonlinear filtering, bioinformatics, complex algebraic geometry, Cauchy–Riemann geometry, and singularities theory. Dr. Yau has been the Managing Editor and Founder of Journal of Algebraic Geometry since 1991 and the Editor-in-Chief and Founder of Communications in Information and Systems since 2000. He was the General Chairman of the 1995 IEEE International Conference on Control and Information. He received the Sloan Fellowship in 1980, the Guggenheim Fellowship in 2000, and the American Mathematical Society Fellow Award in 2013. In 2005, he was entitled the UIC Distinguished Professor. In 2019, He won the Chern Prize of Lifetime Achievement in Mathematics.

Xiuqiong Chen received the B.S. degree in the School of Mathematical Sciences, Beihang University, Beijing, China, in 2014, and the Ph.D. degree in applied mathematics from the Department of Mathematical Sciences, Tsinghua University, Beijing, China in 2019. After her graduation, she was a Postdoctoral Scholar with Yau Mathematical Sciences Center, Tsinghua University, Beijing, China, from 2019 to 2021. She joined in Renmin University of China, Beijing, China, since 2021. She is currently an Assistant Professor with School of Mathematics, Renmin University of China. Her research interests include nonlinear filtering and deep learning.

Xiaopei Jiao received his Bachelor's degree from Shanghai Jiao Tong University in 2017 and completed his Ph.D. from the Department of Mathematics at Tsinghua University in 2022. From 2022 to 2024, he worked as a postdoctoral researcher at the Beijing Institute of Mathematical Science and Application. He is currently employed at the University of Twente in the Netherlands as postdoctoral researcher. His research focuses on nonlinear filtering, Lie estimation algebra, physics-informed deep learning, and bioinformatics.

Jiayi Kang received the B.S. degree from the college of mathematics, Sichuan University, Chengdu, China, in 2019 and Ph.D. degree from Department of Mathematical Sciences at Tsinghua University, China in 2024. He is currently an assistant professor at the Beijing Institute of Mathematical Sciences and Applications in Beijing, China.

Zeju Sun received the B.S. degree from Department of Mathematical Sciences, Tsinghua University, Beijing, China, in 2020. He is currently pursuing the Ph.D. degree in mathematics with the department of Mathematical Sciences, Tsinghua University, Beijing, China.

Yangtianze Tao received the B.S degree in College of Mathematics, Sichuan University, Sichuan, China in 2019. Now he is pursuing Ph.D. degree with Department of Mathematical Sciences, Tsinghua University, Beijing, China, under the supervision of Prof. Stephen Yau in the field of applied mathematics. His research interests include deep learning, machine learning and nonlinear filtering.

"Sobre este título" puede pertenecer a otra edición de este libro.

Detalles bibliográficos

Título: Principles of Nonlinear Filtering Theory (...
Editorial: Springer
Año de publicación: 2024
Encuadernación: Encuadernación de tapa dura
Condición: New

Los mejores resultados en AbeBooks

Imagen del vendedor

Yau, Stephen S.-T.; Chen, Xiuqiong; Jiao, Xiaopei; Kang, Jiayi; Sun, Zeju; Tao, Yangtianze
Publicado por Springer, 2024
ISBN 10: 3031776836 ISBN 13: 9783031776830
Antiguo o usado Tapa dura

Librería: Goodbooks Company, Springdale, AR, Estados Unidos de America

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Condición: acceptable. This copy may contain significant wear, including bending, heavy writing, tears, and or water damage. This book is a functional copy, not necessarily a beautiful copy. Copy may have loose pages. May not include access codes or CDs. May be an Ex library book with stickers and stamps. Dustjacket may be missing. Nº de ref. del artículo: GBV.3031776836.A

Contactar al vendedor

Comprar usado

EUR 36,21
EUR 2,55 shipping
Se envía dentro de Estados Unidos de America

Cantidad disponible: 2 disponibles

Añadir al carrito

Imagen del vendedor

Yau, Stephen S.-T.; Chen, Xiuqiong; Jiao, Xiaopei; Kang, Jiayi; Sun, Zeju; Tao, Yangtianze
Publicado por Springer Verlag GmbH, 2024
ISBN 10: 3031776836 ISBN 13: 9783031776830
Nuevo Tapa dura
Impresión bajo demanda

Librería: moluna, Greven, Alemania

Calificación del vendedor: 4 de 5 estrellas Valoración 4 estrellas, Más información sobre las valoraciones de los vendedores

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Nº de ref. del artículo: 1917682087

Contactar al vendedor

Comprar nuevo

EUR 60,06
EUR 48,99 shipping
Se envía de Alemania a Estados Unidos de America

Cantidad disponible: Más de 20 disponibles

Añadir al carrito

Imagen del vendedor

Stephen S. -T. Yau (u. a.)
Publicado por Springer, 2024
ISBN 10: 3031776836 ISBN 13: 9783031776830
Nuevo Tapa dura
Impresión bajo demanda

Librería: preigu, Osnabrück, Alemania

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Buch. Condición: Neu. Principles of Nonlinear Filtering Theory | Stephen S. -T. Yau (u. a.) | Buch | xvii | Englisch | 2024 | Springer | EAN 9783031776830 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. Nº de ref. del artículo: 130811716

Contactar al vendedor

Comprar nuevo

EUR 62,45
EUR 70,00 shipping
Se envía de Alemania a Estados Unidos de America

Cantidad disponible: 5 disponibles

Añadir al carrito

Imagen de archivo

Yau, Stephen S.-T. (Author)/ Chen, Xiuqiong (Author)/ Jiao, Xiaopei (Author)/ Kang, Jiayi (Author)/ Sun, Zeju (Author)/ Tao, Yangtianze (Author)
Publicado por Springer, 2024
ISBN 10: 3031776836 ISBN 13: 9783031776830
Nuevo Tapa dura
Impresión bajo demanda

Librería: Revaluation Books, Exeter, Reino Unido

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Hardcover. Condición: Brand New. 487 pages. 9.25x6.10x9.21 inches. In Stock. This item is printed on demand. Nº de ref. del artículo: __3031776836

Contactar al vendedor

Comprar nuevo

EUR 64,51
EUR 14,24 shipping
Se envía de Reino Unido a Estados Unidos de America

Cantidad disponible: 1 disponibles

Añadir al carrito

Imagen de archivo

Stephen S.-T. Yau
ISBN 10: 3031776836 ISBN 13: 9783031776830
Nuevo Tapa dura

Librería: CitiRetail, Stevenage, Reino Unido

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Hardcover. Condición: new. Hardcover. This text presents a comprehensive and unified treatment of nonlinear filtering theory, with a strong emphasis on its mathematical underpinnings. It is tailored to meet the needs of a diverse readership, including mathematically inclined engineers and scientists at both graduate and post-graduate levels. What sets this book apart from other treatments of the topic is twofold. Firstly, it offers a complete treatment of filtering theory, providing readers with a thorough understanding of the subject. Secondly, it introduces updated methodologies and applications that are crucial in todays landscape. These include finite-dimensional filters, the Yau-Yau algorithm, direct methods, and the integration of deep learning with filtering problems. The book will be an invaluable resource for researchers and practitioners for years to come.With a rich historical backdrop dating back to Gauss and Wiener, the exposition delves into the fundamental principles underpinning the estimation of stochastic processes amidst noisy observationsa critical tool in various applied domains such as aircraft navigation, solar mapping, and orbit determination, to name just a few. Substantive exercises and examples given in each chapter provide the reader with opportunities to appreciate applications and ample ways to test their understanding of the topics covered. An especially nice feature for those studying the subject independent of a traditional course setting is the inclusion of solutions to exercises at the end of the book.The book is structured into three cohesive parts, each designed to build the reader's understanding of nonlinear filtering theory. In the first part, foundational concepts from probability theory, stochastic processes, stochastic differential equations, and optimization are introduced, providing readers with the necessary mathematical background. The second part delves into theoretical aspects of filtering theory, covering topics such as the stochastic partial differential equation governing the posterior density function of the state, and the estimation algebra theory of systems with finite-dimensional filters. Moving forward, the third part of the book explores numerical algorithms for solving filtering problems, including the Yau-Yau algorithm, direct methods, classical filtering algorithms like the particle filter, and the intersection of filtering theory with deep learning. Moving forward, the third part of the book explores numerical algorithms for solving filtering problems, including the Yau-Yau algorithm, direct methods, classical filtering algorithms like the particle filter, and the intersection of filtering theory with deep learning. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9783031776830

Contactar al vendedor

Comprar nuevo

EUR 65,09
EUR 42,14 shipping
Se envía de Reino Unido a Estados Unidos de America

Cantidad disponible: 1 disponibles

Añadir al carrito

Imagen del vendedor

Stephen S. -T. Yau
Publicado por Springer Nature Switzerland, 2024
ISBN 10: 3031776836 ISBN 13: 9783031776830
Nuevo Tapa dura

Librería: AHA-BUCH GmbH, Einbeck, Alemania

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This text presents a comprehensive and unified treatment of nonlinear filtering theory, with a strong emphasis on its mathematical underpinnings. It is tailored to meet the needs of a diverse readership, including mathematically inclined engineers and scientists at both graduate and post-graduate levels. What sets this book apart from other treatments of the topic is twofold. Firstly, it offers a complete treatment of filtering theory, providing readers with a thorough understanding of the subject. Secondly, it introduces updated methodologies and applications that are crucial in today's landscape. These include finite-dimensional filters, the Yau-Yau algorithm, direct methods, and the integration of deep learning with filtering problems. The book will be an invaluable resource for researchers and practitioners for years to come.With a rich historical backdrop dating back to Gauss and Wiener, the exposition delves into the fundamental principles underpinning the estimation of stochastic processes amidst noisy observations-a critical tool in various applied domains such as aircraft navigation, solar mapping, and orbit determination, to name just a few. Substantive exercises and examples given in each chapter provide the reader with opportunities to appreciate applications and ample ways to test their understanding of the topics covered.An especially nice feature for those studying the subject independent of a traditional course setting is the inclusion of solutions to exercises at the end of the book.The book is structured into three cohesive parts, each designed to build the reader's understanding of nonlinear filtering theory. In the first part, foundational concepts from probability theory, stochastic processes, stochastic differential equations, and optimization are introduced, providing readers with the necessary mathematical background. The second part delves into theoretical aspects of filtering theory, covering topics such as the stochastic partial differential equation governing the posterior density function of the state, and the estimation algebra theory of systems with finite-dimensional filters. Moving forward, the third part of the book explores numerical algorithms for solving filtering problems, including the Yau-Yau algorithm, direct methods, classical filtering algorithms like the particle filter, and the intersection of filtering theory with deep learning. Nº de ref. del artículo: 9783031776830

Contactar al vendedor

Comprar nuevo

EUR 69,54
EUR 64,46 shipping
Se envía de Alemania a Estados Unidos de America

Cantidad disponible: 1 disponibles

Añadir al carrito

Imagen del vendedor

Stephen S. -T. Yau
ISBN 10: 3031776836 ISBN 13: 9783031776830
Nuevo Tapa dura

Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Buch. Condición: Neu. Neuware -This text presents a comprehensive and unified treatment of nonlinear filtering theory, with a strong emphasis on its mathematical underpinnings. It is tailored to meet the needs of a diverse readership, including mathematically inclined engineers and scientists at both graduate and post-graduate levels. What sets this book apart from other treatments of the topic is twofold. Firstly, it offers a complete treatment of filtering theory, providing readers with a thorough understanding of the subject. Secondly, it introduces updated methodologies and applications that are crucial in today's landscape. These include finite-dimensional filters, the Yau-Yau algorithm, direct methods, and the integration of deep learning with filtering problems. The book will be an invaluable resource for researchers and practitioners for years to come.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 488 pp. Englisch. Nº de ref. del artículo: 9783031776830

Contactar al vendedor

Comprar nuevo

EUR 69,54
EUR 60,00 shipping
Se envía de Alemania a Estados Unidos de America

Cantidad disponible: 2 disponibles

Añadir al carrito

Imagen del vendedor

Stephen S.-T. Yau
ISBN 10: 3031776836 ISBN 13: 9783031776830
Nuevo Tapa dura
Impresión bajo demanda

Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This text presents a comprehensive and unified treatment of nonlinear filtering theory, with a strong emphasis on its mathematical underpinnings. It is tailored to meet the needs of a diverse readership, including mathematically inclined engineers and scientists at both graduate and post-graduate levels. What sets this book apart from other treatments of the topic is twofold. Firstly, it offers a complete treatment of filtering theory, providing readers with a thorough understanding of the subject. Secondly, it introduces updated methodologies and applications that are crucial in today's landscape. These include finite-dimensional filters, the Yau-Yau algorithm, direct methods, and the integration of deep learning with filtering problems. The book will be an invaluable resource for researchers and practitioners for years to come.With a rich historical backdrop dating back to Gauss and Wiener, the exposition delves into the fundamental principles underpinning the estimation of stochastic processes amidst noisy observations-a critical tool in various applied domains such as aircraft navigation, solar mapping, and orbit determination, to name just a few. Substantive exercises and examples given in each chapter provide the reader with opportunities to appreciate applications and ample ways to test their understanding of the topics covered.An especially nice feature for those studying the subject independent of a traditional course setting is the inclusion of solutions to exercises at the end of the book.The book is structured into three cohesive parts, each designed to build the reader's understanding of nonlinear filtering theory. In the first part, foundational concepts from probability theory, stochastic processes, stochastic differential equations, and optimization are introduced, providing readers with the necessary mathematical background. The second part delves into theoretical aspects of filtering theory, covering topics such as the stochastic partial differential equation governing the posterior density function of the state, and the estimation algebra theory of systems with finite-dimensional filters. Moving forward, the third part of the book explores numerical algorithms for solving filtering problems, including the Yau-Yau algorithm, direct methods, classical filtering algorithms like the particle filter, and the intersection of filtering theory with deep learning. 470 pp. Englisch. Nº de ref. del artículo: 9783031776830

Contactar al vendedor

Comprar nuevo

EUR 69,54
EUR 23,00 shipping
Se envía de Alemania a Estados Unidos de America

Cantidad disponible: 2 disponibles

Añadir al carrito

Imagen de archivo

Unknown, Unknown
ISBN 10: 3031776836 ISBN 13: 9783031776830
Nuevo

Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Condición: New. Nº de ref. del artículo: 49443384-n

Contactar al vendedor

Comprar nuevo

EUR 72,14
EUR 2,25 shipping
Se envía dentro de Estados Unidos de America

Cantidad disponible: Más de 20 disponibles

Añadir al carrito

Imagen de archivo

Unknown, Unknown
ISBN 10: 3031776836 ISBN 13: 9783031776830
Nuevo

Librería: GreatBookPricesUK, Woodford Green, Reino Unido

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Condición: New. Nº de ref. del artículo: 49443384-n

Contactar al vendedor

Comprar nuevo

EUR 72,69
EUR 17,08 shipping
Se envía de Reino Unido a Estados Unidos de America

Cantidad disponible: Más de 20 disponibles

Añadir al carrito

Existen otras 9 copia(s) de este libro

Ver todos los resultados de su búsqueda