| 000 | 02713nam a22004338i 4500 | ||
|---|---|---|---|
| 005 | 20250919090121.0 | ||
| 006 | m|||||o||d|||||||| | ||
| 007 | cr|||||||||||| | ||
| 008 | 170308s2015||||enk o ||1 0|eng|d | ||
| 020 | _a9781107706804 (ebook) | ||
| 020 | _z9781107069398 (hardback) | ||
| 020 | _z9781107663916 (paperback) | ||
| 035 | _a(UkCbUP)CR9781107706804 | ||
| 039 | 9 |
_y03-08-2017 _zhafiz |
|
| 040 |
_aUkCbUP _beng _erda _cUkCbUP |
||
| 050 | 0 | 0 |
_aQA279.5 _b.R45 2015 |
| 082 | 0 | 0 |
_a519.2 _223 |
| 100 | 1 |
_aReich, Sebastian, _eauthor. |
|
| 245 | 1 | 0 |
_aProbabilistic Forecasting and Bayesian Data Assimilation / _cSebastian Reich, Colin Cotter. |
| 246 | 3 | _aProbabilistic Forecasting & Bayesian Data Assimilation | |
| 264 | 1 |
_aCambridge : _bCambridge University Press, _c2015. |
|
| 300 |
_a1 online resource (308 pages) : _bdigital, PDF file(s). |
||
| 336 |
_atext _btxt _2rdacontent |
||
| 337 |
_acomputer _bc _2rdamedia |
||
| 338 |
_aonline resource _bcr _2rdacarrier |
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| 500 | _aTitle from publisher's bibliographic system (viewed on 07 Mar 2017). | ||
| 520 | _aIn this book the authors describe the principles and methods behind probabilistic forecasting and Bayesian data assimilation. Instead of focusing on particular application areas, the authors adopt a general dynamical systems approach, with a profusion of low-dimensional, discrete-time numerical examples designed to build intuition about the subject. Part I explains the mathematical framework of ensemble-based probabilistic forecasting and uncertainty quantification. Part II is devoted to Bayesian filtering algorithms, from classical data assimilation algorithms such as the Kalman filter, variational techniques, and sequential Monte Carlo methods, through to more recent developments such as the ensemble Kalman filter and ensemble transform filters. The McKean approach to sequential filtering in combination with coupling of measures serves as a unifying mathematical framework throughout Part II. Assuming only some basic familiarity with probability, this book is an ideal introduction for graduate students in applied mathematics, computer science, engineering, geoscience and other emerging application areas. | ||
| 650 | 0 | _aBayesian statistical decision theory | |
| 650 | 0 | _aProbabilities | |
| 650 | 0 | _aUncertainty (Information theory) | |
| 700 | 1 |
_aCotter, Colin, _eauthor. |
|
| 776 | 0 | 8 |
_iPrint version: _z9781107069398 |
| 856 | 4 | 0 | _uhttps://eresourcesptsl.ukm.remotexs.co/user/login?url=https://doi.org/10.1017/CBO9781107706804 |
| 907 |
_a.b16442623 _b2022-10-31 _c2019-11-12 |
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| 942 | _n0 | ||
| 914 | _avtls003618621 | ||
| 998 |
_anone _b2017-08-03 _cm _da _feng _genk _y0 _z.b16442623 |
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| 999 |
_c617478 _d617478 |
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