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01406nam a2200325 a 4500 |
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1227794 |
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20171111232634.0 |
| 008 |
111005s2009 gr b erb 001 0 eng d |
| 020 |
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|a 9780470180945
|q cloth
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| 040 |
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|a GrAtEKP.sci
|b gre
|e AACR2
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| 082 |
|
0 |
|2 22
|a 621.3822
|
| 100 |
1 |
|
|a Candy, James V.
|
| 245 |
1 |
0 |
|a Bayesian signal processing :
|b classical, modern, and particle filtering methods /
|c James V. Candy
|
| 260 |
|
|
|a Hoboken, N.J.:
|b Wiley:
|c c2009
|b IEEE,
|
| 300 |
|
|
|a xxiii, 445 p. :
|b ill., map ;
|c 25 cm.
|
| 490 |
0 |
|
|a Adaptive and learning systems for signal processing, communications, and control
|
| 504 |
|
|
|a Includes bibliographical references and index.
|
| 505 |
0 |
|
|a Bayestian estimation
|
| 505 |
0 |
|
|a Simulation-based Bayesian methods
|
| 505 |
0 |
|
|a State-space models for Bayesian processing
|
| 505 |
0 |
|
|a Classical Bayesian state-space processors
|
| 505 |
0 |
|
|a Modern Bayesian state-space processors
|
| 505 |
0 |
|
|a Particle-based Bayesian state-space processors
|
| 505 |
0 |
|
|a Joint Bayesian state/parametric processors
|
| 505 |
0 |
|
|a Discrete hidden Markov model Bayesian processors
|
| 505 |
0 |
|
|a Bayesian processors for physics-based applications
|
| 505 |
0 |
|
|a Probability & statistics overview
|
| 650 |
|
0 |
|a Signal processing
|x Mathematics
|
| 650 |
|
0 |
|a Bayesian statistical decision theory
|
| 952 |
|
|
|a GrAtEKP
|b 59ccf74d6c5ad134460d5f22
|c 998a
|d 945l
|e 621.3822 CanJ b 2009
|t 1
|x m
|z Books
|
