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Theory and Principles of Smoothing, Filtering and Prediction

A descriptive account based on the theory as well as principles of smoothing, filtering and prediction techniques has been presented;

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Smoothing, Filtering and Prediction

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Smoothing, Filtering and Prediction

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Stochastic Processes & Filtering Theory

, prediction, and smoothing. He presents the mathematical solutions to nonlinear filtering problems, and he specializes the nonlinear theory to linear;

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Periodic Systems

robust stability; the problem of state estimation in its various forms, filtering, prediction and smoothing; control design methods, particularly;

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The Theory of Linear Prediction

. For example, the theory of vector linear prediction is explained in considerable detail and so is the theory of line spectral processes. This;

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Adaptive Filtering Prediction and Control

This unified survey of the theory of adaptive filtering, prediction, and control focuses on linear discrete-time systems and explores the;

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Optimal Filtering

communication systems and digital filtering theory. Vital for students in the fields of control and communications, its contents are also relevant to;

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Adaptive Filtering

This book titled Adaptive Filtering: Principles, Concepts and Applications covers principles, concepts and applications of adaptive;

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Institute of Mathematical Statistics Textbooks

Filtering and smoothing methods are used to produce an accurate estimate of the state of a time-varying system based on multiple;

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Institute of Mathematical Statistics Textbooks

Filtering and smoothing methods are used to produce an accurate estimate of the state of a time-varying system based on multiple;

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Advanced Mathematical Tools for Automatic Control Engineers: Volume 2

techniques for dynamic models and filtering, prediction, and smoothing problems. It also discusses the stochastic approximation method and the robust;

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GPS-Based Optimal FIR Filtering of Clock Models

an efficient way of providing stable and accurate filtering, smoothing, prediction, and steering of the local clock errors is to use the;

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Inference in Hidden Markov Models

This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory. Topics;

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Time Series and Dynamic Models

, multipliers, cointegration and fractionally integrated models. The final section describes the main contribution of filtering and smoothing theory to;

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Time Series And Dynamic Models

, multipliers, cointegration and fractionally integrated models. The final section describes the main contribution of filtering and smoothing theory to;

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Econometrics of Qualitative Dependent Variables

, multipliers, cointegration and fractionally integrated models. The final section describes the main contribution of filtering and smoothing theory to;

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Kernel Smoothing

, Kernel Smoothing: Principles, Methods and Applications offers a user-friendly presentation of the mathematical content so that the reader can;

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Noise Filtering for Big Data Analytics

. If, after the application of any form of smoothing or filtering, the memory of the corresponding data changes heavily, then the final data;

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Forecasting with Exponential Smoothing

Exponential smoothing methods have been around since the 1950s, and are still the most popular forecasting methods used in business and;

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Smoothing Splines

A general class of powerful and flexible modeling techniques, spline smoothing has attracted a great deal of research attention in recent;

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Smoothing Splines

A general class of powerful and flexible modeling techniques, spline smoothing has attracted a great deal of research attention in recent;

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Predicting the Future

Develops a general theory of prediction that encompasses its fundamental principles, methodology, and practice and gives an overview of its;

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Filtering Complex Turbulent Systems

phenomena involves filtering noisy data and prediction based on incomplete information (complicated by the sheer number of parameters involved), and;

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Identification Of Time-Varying Processes

control and failure detection). The processes also assist signal processing in areas such as adaptive noise reduction, prediction of time series;

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Recommender Systems

looks at how this theory is applied and implemented in actual systems. The book examines several classes of recommendation algorithms;

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Quantitative Millimetre Wavelength Spectrometry

both cryogenically cooled and room temperature detectors. Post-detector signal processing, smoothing, filtering and spectral profile fitting;

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