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Bayesian Filtering and Smoothing, Lennart Svensson, Simo Sarkka


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Цена: 5542.00р.
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Автор: Lennart Svensson, Simo Sarkka
Название:  Bayesian Filtering and Smoothing
ISBN: 9781108926645
Издательство: Cambridge Academ
Классификация:







ISBN-10: 1108926649
Обложка/Формат: Paperback
Страницы: 430
Вес: 0.57 кг.
Дата издания: 15.06.2023
Серия: Institute of mathematical statistics textbooks
Язык: English
Издание: 2 revised edition
Иллюстрации: Worked examples or exercises
Размер: 152 x 229 x 19
Ключевые слова: Probability & statistics, MATHEMATICS / Probability & Statistics / General
Ссылка на Издательство: Link
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Поставляется из: Англии
Описание: Now in its second edition, this accessible text presents a unified Bayesian treatment of state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models. The book focuses on discrete-time state space models and carefully introduces fundamental aspects related to optimal filtering and smoothing. In particular, it covers a range of efficient non-linear Gaussian filtering and smoothing algorithms, as well as Monte Carlo-based algorithms. This updated edition features new chapters on constructing state space models of practical systems, the discretization of continuous-time state space models, Gaussian filtering by enabling approximations, posterior linearization filtering, and the corresponding smoothers. Coverage of key topics is expanded, including extended Kalman filtering and smoothing, and parameter estimation. The books practical, algorithmic approach assumes only modest mathematical prerequisites, suitable for graduate and advanced undergraduate students. Many examples are included, with Matlab and Python code available online, enabling readers to implement algorithms in their own projects.


      Старое издание

Practical Smoothing: The Joys of P-splines

Автор: Paul H.C. Eilers, Brian D. Marx
Название: Practical Smoothing: The Joys of P-splines
ISBN: 1108482953 ISBN-13(EAN): 9781108482950
Издательство: Cambridge Academ
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Цена: 8554.00 р.
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Описание: P-splines are widely used in statistics and machine learning for smoothing out noise in data and to avoid overtraining. This practical guide covers theory and a range of standard and non-standard applications with code in R for professionals and researchers looking for a simple, flexible and powerful smoothing tool.

Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking

Автор: Van Trees
Название: Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking
ISBN: 0470120959 ISBN-13(EAN): 9780470120958
Издательство: Wiley
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Цена: 24404.00 р.
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Описание: Bayesian Bounds provides a collection of the important papers dealing with the theory and application of Bayesian bounds.  The book will be useful to both engineers and statisticians whether they are practicioners or theorists. The organization of the book and selection criteria is covered in the preface.  Each part is introduced with the contributions of each selected paper and their interrelationship. Part 1contains a short history of Reverend Thomas Bayes and his classic paper that established the field. Part 2 contains the original derivation of the Bayesian Cramer-Rao bound and a simple derivation of the multiple parameter Bayesian CRB. Part 3 discusses global Bayesian bounds to provide broad coverage of this important area. Part 4 considers the case in which some of the parameters are deterministic and some are random.  Hybrid Bayesian bounds are derived, as they are particularly important in the study of model mismatch problems. Part 5 considers generalized Cramer-Rao bounds. Part 6 discusses nonlinear stochastic dynamic systems. This type of system is a major component of most radar, sonar, and navigation systems.  They are also encountered in nonlinear filtering problems. Applications of various Bayesian bounds to static parameter estimation problems are covered in Part 7 and to dynamic systems in Part 8. The book concludes with papers from the statistics literature that focus on Bayesian bounds in various models in Part 9. 

Filtering and Control of Stochastic Jump Hybrid Systems

Автор: Yao Xiuming, Wu Ligang, Zheng Wei Xing
Название: Filtering and Control of Stochastic Jump Hybrid Systems
ISBN: 3319811525 ISBN-13(EAN): 9783319811529
Издательство: Springer
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Цена: 13974.00 р.
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Описание: Specifically, the considered stochastic jump hybrid systems include Markovian jump Ito stochastic systems, Markovian jump linear-parameter-varying (LPV) systems, Markovian jump singular systems, Markovian jump two-dimensional (2-D) systems, and Markovian jump repeated scalar nonlinear systems.

Spatial autocorrelation and spatial filtering

Автор: Griffith, Daniel A.
Название: Spatial autocorrelation and spatial filtering
ISBN: 3642056660 ISBN-13(EAN): 9783642056666
Издательство: Springer
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Цена: 19589.00 р.
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Описание: Scientific visualization may be defined as the transformation of numerical scientific data into informative graphical displays. The focus is on how scientific visualization can help revolutionize the manner in which the tendencies for (dis)similar numerical values to cluster together in location on a map are explored and analyzed.

Control and Filtering for Semi-Markovian Jump Systems

Автор: Fanbiao Li; Peng Shi; Ligang Wu
Название: Control and Filtering for Semi-Markovian Jump Systems
ISBN: 3319471988 ISBN-13(EAN): 9783319471983
Издательство: Springer
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Цена: 18167.00 р.
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Описание: This book presents up-to-date research developments and novel methodologies on semi-Markovian jump systems (S-MJS). It presents solutions to a series of problems with new approaches for the control and filtering of S-MJS, including stability analysis, sliding mode control, dynamic output feedback control, robust filter design, and fault detection. A set of newly developed techniques such as piecewise analysis method, positively invariant set approach, event-triggered method, and cone complementary linearization approaches are presented. Control and Filtering for Semi-Markovian Jump Systems is a comprehensive reference for researcher and practitioners working in control engineering, system sciences and applied mathematics, and is also a useful source of information for senior undergraduates and graduates in these areas. The readers will benefit from some new concepts, new models and new methodologies with practical significance in control engineering and signal processing.

Time-Dependent Switched Discrete-Time Linear Systems: Control and Filtering

Автор: Zhang Lixian, Zhu Yanzheng, Shi Peng
Название: Time-Dependent Switched Discrete-Time Linear Systems: Control and Filtering
ISBN: 3319804405 ISBN-13(EAN): 9783319804408
Издательство: Springer
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Цена: 13974.00 р.
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Описание: This book focuses on the basic control and filtering synthesis problems for discrete-time switched linear systems under time-dependent switching signals.

Stochastic Evolution Systems

Автор: Rozovsky
Название: Stochastic Evolution Systems
ISBN: 331994892X ISBN-13(EAN): 9783319948928
Издательство: Springer
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Цена: 11878.00 р.
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Описание: Covering the general theory of linear stochastic evolution systems with unbounded drift and diffusion operators, this book sureys Ito`s second-order parabolic equations and explores filtering problems for processes whose trajectories can be described by them.

Spatial Filtering Velocimetry

Автор: Yoshihisa Aizu; Toshimitsu Asakura
Название: Spatial Filtering Velocimetry
ISBN: 3642066402 ISBN-13(EAN): 9783642066405
Издательство: Springer
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Цена: 19589.00 р.
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Описание: In comparison with laser Doppler velocimetry, spatial ?ltering velocimetry had not received much attention from investigators but was studied steadily by several research groups mainly in Japan and is now practically used in various ?elds of engineering.

Using Artificial Neural Networks for Timeseries Smoothing and Forecasting: Case Studies in Economics

Автор: Vrbka Jaromнr
Название: Using Artificial Neural Networks for Timeseries Smoothing and Forecasting: Case Studies in Economics
ISBN: 3030756483 ISBN-13(EAN): 9783030756482
Издательство: Springer
Цена: 20962.00 р.
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Описание: The aim of this publication is to identify and apply suitable methods for analysing and predicting the time series of gold prices, together with acquainting the reader with the history and characteristics of the methods and with the time series issues in general.

Stochastic Analysis, Filtering, and Stochastic Optimization: A Commemorative Volume to Honor Mark H. A. Davis`s Contributions

Автор: Yin George, Zariphopoulou Thaleia
Название: Stochastic Analysis, Filtering, and Stochastic Optimization: A Commemorative Volume to Honor Mark H. A. Davis`s Contributions
ISBN: 3030985180 ISBN-13(EAN): 9783030985189
Издательство: Springer
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Цена: 18167.00 р.
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Описание: 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.

Bayesian Inference of State Space Models: Kalman Filtering and Beyond

Автор: Triantafyllopoulos Kostas
Название: Bayesian Inference of State Space Models: Kalman Filtering and Beyond
ISBN: 3030761231 ISBN-13(EAN): 9783030761233
Издательство: Springer
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Цена: 11179.00 р.
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Описание: Bayesian Inference of State Space Models: Kalman Filtering and Beyond offers a comprehensive introduction to Bayesian estimation and forecasting for state space models.

Stable and Efficient Cubature-Based Filtering in Dynamical Systems

Автор: Ballreich Dominik
Название: Stable and Efficient Cubature-Based Filtering in Dynamical Systems
ISBN: 3319872397 ISBN-13(EAN): 9783319872391
Издательство: Springer
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Цена: 11878.00 р.
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Описание: The book addresses the problem of calculation of d-dimensional integrals (conditional expectations) in filter problems. It develops new methods of deterministic numerical integration, which can be used to speed up and stabilize filter algorithms.


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