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007			cr nn 008mamaa
008			220601s2008 sz | s |||| 0|eng d
020			_a9783031025273
024	7		_a10.1007/978-3-031-02527-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA279.2 _b2008 EB
100	1		_aVaidyanathan, P. P. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687633
245	1	4	_aThe Theory of Linear Prediction _cby P. Vaidyanathan
250			_a1st edition 2008
264		1	_aCham _bSpringer International Publishing _c2008
300			_a1 recurso en línea (XIV, 183 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Signal Processing _x1932-1694
505	0		_aIntroduction -- The Optimal Linear Prediction Problem -- Levinson's Recursion -- Lattice Structures for Linear Prediction -- Autoregressive Modeling -- Prediction Error Bound and Spectral Flatness -- Line Spectral Processes -- Linear Prediction Theory for Vector Processes -- Appendix A: Linear Estimation of Random Variables -- B: Proof of a Property of Autocorrelations -- C: Stability of the Inverse Filter -- Recursion Satisfied by AR Autocorrelations.
520			_aLinear prediction theory has had a profound impact in the field of digital signal processing. Although the theory dates back to the early 1940s, its influence can still be seen in applications today. The theory is based on very elegant mathematics and leads to many beautiful insights into statistical signal processing. Although prediction is only a part of the more general topics of linear estimation, filtering, and smoothing, this book focuses on linear prediction. This has enabled detailed discussion of a number of issues that are normally not found in texts. For example, the theory of vector linear prediction is explained in considerable detail and so is the theory of line spectral processes. This focus and its small size make the book different from many excellent texts which cover the topic, including a few that are actually dedicated to linear prediction. There are several examples and computer-based demonstrations of the theory. Applications are mentioned wherever appropriate, but the focus is not on the detailed development of these applications. The writing style is meant to be suitable for self-study as well as for classroom use at the senior and first-year graduate levels. The text is self-contained for readers with introductory exposure to signal processing, random processes, and the theory of matrices, and a historical perspective and detailed outline are given in the first chapter. Table of Contents: Introduction / The Optimal Linear Prediction Problem / Levinson's Recursion / Lattice Structures for Linear Prediction / Autoregressive Modeling / Prediction Error Bound and Spectral Flatness / Line Spectral Processes / Linear Prediction Theory for Vector Processes / Appendix A: Linear Estimation of Random Variables / B: Proof of a Property of Autocorrelations / C: Stability of the Inverse Filter / Recursion Satisfied by AR Autocorrelations.
988			_aSynthesis Collection of Technology_2008
650		7	_2embne _9150608 _aProceso de señales
650		7	_2embne _9670862 _aPredicción, Teoría de la
776	0	8	_iPrinted edition: _z9783031013997
776	0	8	_iPrinted edition: _z9783031036552
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02527-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI