Theory And Principles Of Smoothing Filtering And Prediction
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Theory And Principles Of Smoothing Filtering And Prediction
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Author : Graham Eanes
language : en
Publisher:
Release Date : 2015-02-23
Theory And Principles Of Smoothing Filtering And Prediction written by Graham Eanes and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-02-23 with categories.
A descriptive account based on the theory as well as principles of smoothing, filtering and prediction techniques has been presented in this book. It aims to provide understanding of classical filtering, prediction techniques and smoothing techniques along with newly developed embellishments for enhancing performance in applications. It describes the domain in a vivid manner for the purpose of serving as a valuable guide for students as well as experts. It extensively discusses minimum-mean-square-error solution construction and asymptotic behavior, continuous-time and discrete-time minimum-variance filtering, minimum-variance filtering results for steady-state problems and continuous-time and discrete-time smoothing. It further elaborates on robust techniques that accommodate uncertainties within problem specifications, parameter estimation, applications of Riccati equations, etc. These afore-mentioned linear techniques have been applied to various nonlinear estimation problems towards the end of the book. Although they have a risk of assurance of optical performance, these mentioned linearizations can be employed in predictors, filters and smoothers. The book serves the objective of imparting practical knowledge amongst students interested in this field.
Principles Of System Identification
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Author : Arun K. Tangirala
language : en
Publisher: CRC Press
Release Date : 2018-10-08
Principles Of System Identification written by Arun K. Tangirala and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-08 with Technology & Engineering categories.
Master Techniques and Successfully Build Models Using a Single Resource Vital to all data-driven or measurement-based process operations, system identification is an interface that is based on observational science, and centers on developing mathematical models from observed data. Principles of System Identification: Theory and Practice is an introductory-level book that presents the basic foundations and underlying methods relevant to system identification. The overall scope of the book focuses on system identification with an emphasis on practice, and concentrates most specifically on discrete-time linear system identification. Useful for Both Theory and Practice The book presents the foundational pillars of identification, namely, the theory of discrete-time LTI systems, the basics of signal processing, the theory of random processes, and estimation theory. It explains the core theoretical concepts of building (linear) dynamic models from experimental data, as well as the experimental and practical aspects of identification. The author offers glimpses of modern developments in this area, and provides numerical and simulation-based examples, case studies, end-of-chapter problems, and other ample references to code for illustration and training. Comprising 26 chapters, and ideal for coursework and self-study, this extensive text: Provides the essential concepts of identification Lays down the foundations of mathematical descriptions of systems, random processes, and estimation in the context of identification Discusses the theory pertaining to non-parametric and parametric models for deterministic-plus-stochastic LTI systems in detail Demonstrates the concepts and methods of identification on different case-studies Presents a gradual development of state-space identification and grey-box modeling Offers an overview of advanced topics of identification namely the linear time-varying (LTV), non-linear, and closed-loop identification Discusses a multivariable approach to identification using the iterative principal component analysis Embeds MATLAB® codes for illustrated examples in the text at the respective points Principles of System Identification: Theory and Practice presents a formal base in LTI deterministic and stochastic systems modeling and estimation theory; it is a one-stop reference for introductory to moderately advanced courses on system identification, as well as introductory courses on stochastic signal processing or time-series analysis.The MATLAB scripts and SIMULINK models used as examples and case studies in the book are also available on the author's website: http://arunkt.wix.com/homepage#!textbook/c397
Bayesian Filtering And Smoothing
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Author : Simo Särkkä
language : en
Publisher: Cambridge University Press
Release Date : 2013-09-05
Bayesian Filtering And Smoothing written by Simo Särkkä and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-09-05 with Computers categories.
A unified Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.
Forecasting Principles And Practice
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Author : Rob J Hyndman
language : en
Publisher: OTexts
Release Date : 2018-05-08
Forecasting Principles And Practice written by Rob J Hyndman and has been published by OTexts this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-08 with Business & Economics categories.
Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.
Adaptive Filtering Prediction And Control
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Author : Graham C Goodwin
language : en
Publisher: Courier Corporation
Release Date : 2014-05-05
Adaptive Filtering Prediction And Control written by Graham C Goodwin and has been published by Courier Corporation this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-05 with Technology & Engineering categories.
This unified survey focuses on linear discrete-time systems and explores natural extensions to nonlinear systems. It emphasizes discrete-time systems, summarizing theoretical and practical aspects of a large class of adaptive algorithms. 1984 edition.
Kalman Filtering Under Information Theoretic Criteria
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Author : Badong Chen
language : en
Publisher: Springer Nature
Release Date : 2023-08-18
Kalman Filtering Under Information Theoretic Criteria written by Badong Chen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-18 with Technology & Engineering categories.
This book provides several efficient Kalman filters (linear or nonlinear) under information theoretic criteria. They achieve excellent performance in complicated non-Gaussian noises with low computation complexity and have great practical application potential. The book combines all these perspectives and results in a single resource for students and practitioners in relevant application fields. Each chapter starts with a brief review of fundamentals, presents the material focused on the most important properties and evaluates comparatively the models discussing free parameters and their effect on the results. Proofs are provided at the end of each chapter. The book is geared to senior undergraduates with a basic understanding of linear algebra, signal processing and statistics, as well as graduate students or practitioners with experience in Kalman filtering.
Optimal Estimation And Information Fusion Theory And Algorithms
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Author : Ming Lei
language : en
Publisher: Springer Nature
Release Date : 2025-08-25
Optimal Estimation And Information Fusion Theory And Algorithms written by Ming Lei and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-25 with Computers categories.
This book mainly focuses on the theme of optimizing estimation and sensor information fusion processing for stochastic dynamic systems. It summarizes the basic theories and methods of optimizing estimation and information fusion direction, including stochastic system models, optimal estimation methods, linear state estimation, nonlinear state estimation, information fusion models, structures, data processing methods, data association based on multi-source data estimation, and other aspects. On the basis of years of teaching practice, the author optimizes the content layout, focuses on the basic theoretical methods of the subject, emphasizes the systematic nature of the theory and the rigor of expression, selectively cuts out some outdated content, and introduces some important and widely accepted new developments in the subject. On the other hand, this book also serves as a reference material for technical developers in this field.
Methods In Biomedical Informatics
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Author : Indra Neil Sarkar
language : en
Publisher: Academic Press
Release Date : 2013-09-03
Methods In Biomedical Informatics written by Indra Neil Sarkar and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-09-03 with Computers categories.
Beginning with a survey of fundamental concepts associated with data integration, knowledge representation, and hypothesis generation from heterogeneous data sets, Methods in Biomedical Informatics provides a practical survey of methodologies used in biological, clinical, and public health contexts. These concepts provide the foundation for more advanced topics like information retrieval, natural language processing, Bayesian modeling, and learning classifier systems. The survey of topics then concludes with an exposition of essential methods associated with engineering, personalized medicine, and linking of genomic and clinical data. Within an overall context of the scientific method, Methods in Biomedical Informatics provides a practical coverage of topics that is specifically designed for: (1) domain experts seeking an understanding of biomedical informatics approaches for addressing specific methodological needs; or (2) biomedical informaticians seeking an approachable overview of methodologies that can be used in scenarios germane to biomedical research. - Contributors represent leading biomedical informatics experts: individuals who have demonstrated effective use of biomedical informatics methodologies in the real-world, high-quality biomedical applications - Material is presented as a balance between foundational coverage of core topics in biomedical informatics with practical "in-the-trenches" scenarios. - Contains appendices that function as primers on: (1) Unix; (2) Ruby; (3) Databases; and (4) Web Services.
Information Theoretic Principles For Agent Learning
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Author : Jerry D. Gibson
language : en
Publisher: Springer Nature
Release Date : 2024-08-05
Information Theoretic Principles For Agent Learning written by Jerry D. Gibson and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-05 with Technology & Engineering categories.
This book provides readers with the fundamentals of information theoretic techniques for statistical data science analyses and for characterizing the behavior and performance of a learning agent outside of the standard results on communications and compression fundamental limits. Readers will benefit from the presentation of information theoretic quantities, definitions, and results that provide or could provide insights into data science and learning.
Machine Learning And Principles And Practice Of Knowledge Discovery In Databases
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Author : Michael Kamp
language : en
Publisher: Springer Nature
Release Date : 2022-02-17
Machine Learning And Principles And Practice Of Knowledge Discovery In Databases written by Michael Kamp and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-17 with Computers categories.
This two-volume set constitutes the refereed proceedings of the workshops which complemented the 21th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2021. Due to the COVID-19 pandemic the conference and workshops were held online. The 104 papers were thoroughly reviewed and selected from 180 papers submited for the workshops. This two-volume set includes the proceedings of the following workshops:Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence (AIMLAI 2021)Workshop on Parallel, Distributed and Federated Learning (PDFL 2021)Workshop on Graph Embedding and Mining (GEM 2021)Workshop on Machine Learning for Irregular Time-series (ML4ITS 2021)Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM 2021)Workshop on eXplainable Knowledge Discovery in Data Mining (XKDD 2021)Workshop on Bias and Fairness in AI (BIAS 2021)Workshop on Workshop on Active Inference (IWAI 2021)Workshop on Machine Learning for Cybersecurity (MLCS 2021)Workshop on Machine Learning in Software Engineering (MLiSE 2021)Workshop on MIning Data for financial applications (MIDAS 2021)Sixth Workshop on Data Science for Social Good (SoGood 2021)Workshop on Machine Learning for Pharma and Healthcare Applications (PharML 2021)Second Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning (EDML 2020)Workshop on Machine Learning for Buildings Energy Management (MLBEM 2021)