| 000 | 02520nam a2200397 i 4500 | ||
|---|---|---|---|
| 001 | CR9781139344203 | ||
| 003 | UkCbUP | ||
| 005 | 20240830155643.0 | ||
| 006 | m|||||o||d|||||||| | ||
| 007 | cr|||||||||||| | ||
| 008 | 120316s2013||||enk o ||1 0|eng|d | ||
| 020 | _a9781139344203 (ebook) | ||
| 020 | _z9781107030657 (hardback) | ||
| 020 | _z9781107619289 (paperback) | ||
| 040 |
_aUkCbUP _beng _erda _cUkCbUP |
||
| 050 | 0 | 0 |
_aQA279.5 _b.S27 2013 |
| 082 | 0 | 0 |
_a519.5/42 _223 |
| 100 | 1 |
_aSärkkä, Simo, _eauthor. |
|
| 245 | 1 | 0 |
_aBayesian filtering and smoothing / _cSimo Särkkä. |
| 246 | 3 | _aBayesian Filtering & Smoothing | |
| 264 | 1 |
_aCambridge : _bCambridge University Press, _c2013. |
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| 300 |
_a1 online resource (xxii, 232 pages) : _bdigital, PDF file(s). |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
||
| 338 |
_aonline resource _bcr _2rdacarrier |
||
| 490 | 1 |
_aInstitute of Mathematical Statistics textbooks ; _v3 |
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| 500 | _aTitle from publisher's bibliographic system (viewed on 05 Oct 2015). | ||
| 520 | _aFiltering 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. Readers learn what non-linear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages. They also discover how state-of-the-art Bayesian parameter estimation methods can be combined with state-of-the-art filtering and smoothing algorithms. The book's practical and algorithmic approach assumes only modest mathematical prerequisites. Examples include Matlab computations, and the numerous end-of-chapter exercises include computational assignments. Matlab code is available for download at www.cambridge.org/sarkka, promoting hands-on work with the methods. | ||
| 650 | 0 | _aBayesian statistical decision theory. | |
| 650 | 0 | _aFilters (Mathematics) | |
| 650 | 0 | _aSmoothing (Statistics) | |
| 776 | 0 | 8 |
_iPrint version: _z9781107030657 |
| 830 | 0 |
_aInstitute of Mathematical Statistics textbooks ; _v3. |
|
| 856 | 4 | 0 | _uhttps://doi.org/10.1017/CBO9781139344203 |
| 942 |
_2ddc _cEB |
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| 999 |
_c9954 _d9954 |
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