Bayesian signal processing : classical, modern, and particle filtering methods /
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| Author / Creator: | Candy, James V. |
|---|---|
| Imprint: | Hoboken, N.J. : Wiley : IEEE, [2009] ©2009 |
| Description: | 1 online resource (xxiii, 445 pages) : illustrations, map |
| Language: | English |
| Series: | Adaptive and learning systems for signal processing, communications, and control Adaptive and learning systems for signal processing, communications, and control. |
| Subject: | |
| Format: | E-Resource Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/13595719 |
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| 100 | 1 | |a Candy, James V. | |
| 245 | 1 | 0 | |a Bayesian signal processing : |b classical, modern, and particle filtering methods / |c James V. Candy. |
| 264 | 1 | |a Hoboken, N.J. : |b Wiley : |b IEEE, |c [2009] | |
| 264 | 4 | |c ©2009 | |
| 300 | |a 1 online resource (xxiii, 445 pages) : |b illustrations, map | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a data file | ||
| 490 | 1 | |a Adaptive and learning systems for signal processing, communications, and control | |
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Bayestian estimation -- Simulation-based Bayesian methods -- State-space models for Bayesian processing -- Classical Bayesian state-space processors -- Modern Bayesian state-space processors -- Particle-based Bayesian state-space processors -- Joint Bayesian state/parametric processors -- Discrete hidden Markov model Bayesian processors -- Bayesian processors for physics-based applications. | |
| 520 | |a New Bayesian approach helps you solve tough problems in signal processing with ease. Signal processing is based on this fundamental conceptthe extraction of critical information from noisy, uncertain data. Most techniques rely on underlying Gaussian assumptions for a solution, but what happens when these assumptions are erroneous? Bayesian techniques circumvent this limitation by offering a completely different approach that can easily incorporate non-Gaussian and nonlinear processes along with all of the usual methods currently available. This text enables readers to fully exploit the many a. | ||
| 650 | 0 | |a Signal processing |x Mathematics. | |
| 650 | 0 | |a Bayesian statistical decision theory. |0 http://id.loc.gov/authorities/subjects/sh85012506 | |
| 650 | 6 | |a Traitement du signal |x Mathématiques. | |
| 650 | 6 | |a Théorie de la décision bayésienne. | |
| 650 | 7 | |a COMPUTERS |x Information Theory. |2 bisacsh | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Signals & Signal Processing. |2 bisacsh | |
| 650 | 7 | |a Bayesian statistical decision theory |2 fast | |
| 650 | 7 | |a Signal processing |x Mathematics |2 fast | |
| 758 | |i has work: |a Bayesian signal processing (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFq67vWvmMh97kyTrgMWym |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Candy, J.V. |t Bayesian signal processing. |d Hoboken, N.J. : Wiley : IEEE, ©2009 |z 9780470180945 |z 0470180943 |w (DLC) 2008032184 |w (OCoLC)230183299 |
| 830 | 0 | |a Adaptive and learning systems for signal processing, communications, and control. |0 http://id.loc.gov/authorities/names/n93053665 | |
| 856 | 4 | 0 | |u https://go.oreilly.com/uchicago/library/view/-/9781118210543/?ar |y O'Reilly |
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| 928 | |t Library of Congress classification |a TK5102.9.C3187 2009 |l Online |c UC-FullText |u https://go.oreilly.com/uchicago/library/view/-/9781118210543/?ar |z O'Reilly |g ebooks |i 13738660 | ||