Bayesian filtering and smoothing / Simo Särkkä.
"Filtering and smoothing methods are used to produce an accurate estimate of the state of a time-varying system based on multiple observational inputs (data). Interest in these methods has exploded in recent years, with numerous applications emerging in fields such as navigation, aerospace engi...
Saved in:
| Main Author: | |
|---|---|
| Format: | Ebook |
| Language: | English |
| Published: |
Cambridge, U.K. ; New York :
Cambridge University Press,
2013.
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| Series: | Institute of Mathematical Statistics textbooks ;
3. |
| Subjects: | |
| Online Access: | Cambridge Books on Core |
MARC
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| 100 | 1 | |a Särkkä, Simo, |e author. |9 1159636 | |
| 245 | 1 | 0 | |a Bayesian filtering and smoothing / |c Simo Särkkä. |
| 264 | 1 | |a Cambridge, U.K. ; |a New York : |b Cambridge University Press, |c 2013. | |
| 300 | |a 1 online resource (xxii, 232 pages) : |b illustrations. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Institute of Mathematical Statistics textbooks ; |v 3 | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a 1. What are Bayesian filtering and smoothing? -- 2. Bayesian inference -- 3. Batch and recursive Bayesian estimation -- 4. Bayesian filtering equations and exact solutions -- 5. Extended and unscented Kalman filtering -- 6. General Gaussian filtering -- 7. Particle filtering -- 8. Bayesian smoothing equations and exact solutions -- 9. Extended and unscented smoothing -- 10. General Gaussian smoothing -- 11. Particle smoothing -- 12. Parameter estimation -- 13. Epilogue. | |
| 520 | |a "Filtering and smoothing methods are used to produce an accurate estimate of the state of a time-varying system based on multiple observational inputs (data). Interest in these methods has exploded in recent years, with numerous applications emerging in fields such as navigation, aerospace engineering, telecommunications and medicine. This compact, informal introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework."--Cover. | ||
| 588 | |a Machine converted from AACR2 source record. | ||
| 650 | 0 | |a Bayesian statistical decision theory. |9 314460 | |
| 650 | 0 | |a Filters (Mathematics) |9 317872 | |
| 650 | 0 | |a Smoothing (Statistics) |9 329290 | |
| 776 | 1 | 8 | |w (OCoLC)840462877 |w (OCoLC)861618312 |
| 830 | 0 | |a Institute of Mathematical Statistics textbooks ; |v 3. |9 922887 | |
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