Universal Coding and Order Identification by Model Selection Methods, Gassiat
Автор: Zhang Guangjun Название: Star Identification: Methods, Techniques and Algorithms ISBN: 3662571587 ISBN-13(EAN): 9783662571583 Издательство: Springer Рейтинг: Цена: 22359.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Introduction.-Fundamental Knowledge of Astronomy.- Introduction of Celestial Navigation.- Introduction of Star Sensor.- Introduction of star identification.- Current star identification algorithms and the development trends.- Content introduction of the chapters.- References.- Processing of Star Catalog and Star Image.- Star catalog partition.- Guide star selecting and double star processing.- Star image simulation.- Star spot centroiding.- Calibration of centroiding error.- References.- Star Identification using Modified Triangle Algorithm.- Current triangle algorithms.- Modified triangle algorithm using angular distance matching.- Modified triangle algorithm using P vector.- References.- Star Identification using Star Patterns.- Introduction of grid algorithm.- Star identification using radial and cyclic star patterns.- Star identification using Log-Polar transform.- Star identification without calibration parameters.- References.- Star Identification using Neural Networks.- Introduction of neural networks.- Neural networks based star identification using features of star vector matrix.- Neural networks based star identification using mixed features.- References.- Rapid Star Tracking using Star Matching between Adjacent Frames.- Star tracking mode of star sensor.- Rapid star tracking algorithm using star matching between adjacent frames.- Simulations and results analysis.- References.- Hardware Implement and Performance Test of Star Identification.- Implement of star identification on RISC CPU.- Hardware-in-loop simulation test of star identification.- Field experiment of star identification.- References.
Описание: The book is divided into four chapters, the first of which introduces readers to lossless coding, provides an intrinsic lower bound on the codeword length in terms of Shannon`s entropy, and presents some coding methods that can achieve this lower bound, provided the source distribution is known.
Автор: Torsten S?derstr?m Название: Errors-in-Variables Methods in System Identification ISBN: 3030091252 ISBN-13(EAN): 9783030091255 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Поставка под заказ.
Описание: This book presents an overview of the different errors-in-variables (EIV) methods that can be used for system identification. Readers will explore the properties of an EIV problem. Such problems play an important role when the purpose is the determination of the physical laws that describe the process, rather than the prediction or control of its future behaviour. EIV problems typically occur when the purpose of the modelling is to get physical insight into a process. Identifiability of the model parameters for EIV problems is a non-trivial issue, and sufficient conditions for identifiability are given. The author covers various modelling aspects which, taken together, can find a solution, including the characterization of noise properties, extension to multivariable systems, and continuous-time models. The book finds solutions that are constituted of methods that are compatible with a set of noisy data, which traditional approaches to solutions, such as (total) least squares, do not find. A number of identification methods for the EIV problem are presented. Each method is accompanied with a detailed analysis based on statistical theory, and the relationship between the different methods is explained. A multitude of methods are covered, including: instrumental variables methods; methods based on bias-compensation; covariance matching methods; and prediction error and maximum-likelihood methods. The book shows how many of the methods can be applied in either the time or the frequency domain and provides special methods adapted to the case of periodic excitation. It concludes with a chapter specifically devoted to practical aspects and user perspectives that will facilitate the transfer of the theoretical material to application in real systems. Errors-in-Variables Methods in System Identification gives readers the possibility of recovering true system dynamics from noisy measurements, while solving over-determined systems of equations, making it suitable for statisticians and mathematicians alike. The book also acts as a reference for researchers and computer engineers because of its detailed exploration of EIV problems.
Автор: S?derstr?m Название: Errors-in-Variables Methods in System Identification ISBN: 3319750003 ISBN-13(EAN): 9783319750002 Издательство: Springer Рейтинг: Цена: 23757.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents an overview of the different errors-in-variables (EIV) methods that can be used for system identification.
Автор: ByoungSeon Choi Название: ARMA Model Identification ISBN: 1461397472 ISBN-13(EAN): 9781461397472 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The main topics covered include: Box-Jenkins` method, inverse autocorrelation functions, penalty function identification such as AIC, BIC techniques and Hannan and Quinn`s method, instrumental regression, and a range of pattern identification methods.
Автор: Hjort Blindell Gabriel Название: Instruction Selection: Principles, Methods, and Applications ISBN: 3319816586 ISBN-13(EAN): 9783319816586 Издательство: Springer Рейтинг: Цена: 5589.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The survey is structured according to two dimensions: approaches to instruction selection from the past 45 years are organized and discussed according to their fundamental principles, and according to the characteristics of the supported machine instructions.
Описание: The textbook on analysis and visualization of social networks that integrates theory, applications, and professional software for performing network analysis. Pajek software and datasets for all examples are freely available, so the reader can learn network analysis by doing it. Each chapter offers case studies for practicing network analysis.
Автор: Oneto Luca Название: Model Selection and Error Estimation in a Nutshell ISBN: 3030243613 ISBN-13(EAN): 9783030243616 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Statistical learning theory answers these questions by deriving non-asymptotic bounds on the generalization error of a model or, in other words, by upper bounding the true error of the learned model based just on quantities computed on the available data.
Автор: Luca Oneto Название: Model Selection and Error Estimation in a Nutshell ISBN: 3030243583 ISBN-13(EAN): 9783030243586 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Поставка под заказ.
Описание: How can we select the best performing data-driven model? How can we rigorously estimate its generalization error? Statistical learning theory answers these questions by deriving non-asymptotic bounds on the generalization error of a model or, in other words, by upper bounding the true error of the learned model based just on quantities computed on the available data. However, for a long time, Statistical learning theory has been considered only an abstract theoretical framework, useful for inspiring new learning approaches, but with limited applicability to practical problems. The purpose of this book is to give an intelligible overview of the problems of model selection and error estimation, by focusing on the ideas behind the different statistical learning theory approaches and simplifying most of the technical aspects with the purpose of making them more accessible and usable in practice. The book starts by presenting the seminal works of the 80’s and includes the most recent results. It discusses open problems and outlines future directions for research.
Автор: Ando, Tomohiro Название: Bayesian Model Selection and Statistical Modeling ISBN: 0367383977 ISBN-13(EAN): 9780367383978 Издательство: Taylor&Francis Рейтинг: Цена: 9798.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Along with many practical applications, Bayesian Model Selection and Statistical Modeling presents an array of Bayesian inference and model selection procedures. It thoroughly explains the concepts, illustrates the derivations of various Bayesian model selection criteria through examples, and provides R code for implementation.
The author shows how to implement a variety of Bayesian inference using R and sampling methods, such as Markov chain Monte Carlo. He covers the different types of simulation-based Bayesian model selection criteria, including the numerical calculation of Bayes factors, the Bayesian predictive information criterion, and the deviance information criterion. He also provides a theoretical basis for the analysis of these criteria. In addition, the author discusses how Bayesian model averaging can simultaneously treat both model and parameter uncertainties.
Selecting and constructing the appropriate statistical model significantly affect the quality of results in decision making, forecasting, stochastic structure explorations, and other problems. Helping you choose the right Bayesian model, this book focuses on the framework for Bayesian model selection and includes practical examples of model selection criteria.
Автор: Antonio Aznar Grasa Название: Econometric Model Selection ISBN: 904814051X ISBN-13(EAN): 9789048140510 Издательство: Springer Рейтинг: Цена: 23757.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book proposes a new methodology for the selection of one (model) from among a set of alternative econometric models.
Описание: This book tells the story of radical transparency in a datafied world. It is a story that not only includes the beginnings of WikiLeaks and its endings as a weapon of the GRU, but also exposes numerous other decentralised disclosure networks designed to crack open democracy - for good or ill - that followed in its wake.
This is a story that can only be understood through rethinking how technologies of government, practices of media, and assumptions of democracy interact. By combining literatures of governmentality, media studies, and democracy, this illuminating account offers novel insights and critiques of the transparency ideal through its material-political practice.
Case studies uncover evolving media practices that, regardless of being scraped from public records or leaked from internal sources, still divulge secrets. The narrative also traces new corporate players such as Clearview AI, the civic-minded ICIJ, and state-based public health disclosures in times of pandemic to reveal how they all form unique proto-institutional instances of disclosure as a technology of government. The analysis of novel forms of digital radical transparency - from a trickle of paper-based leaks to the modern digital .torrent - is grounded in analogues from the analogue past, which combine to tell the whole story of how transparency functions in and helps form democracy.
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