000			02663nam a22004098i 4500
001			CR9781107706804
005			20250919142054.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			131106s2015||||enk o ||1 0|eng|d
020			_a9781107706804 (ebook)
020			_z9781107069398 (hardback)
020			_z9781107663916 (paperback)
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, University of Potsdam and University of Reading, Colin Cotter, Imperial College, London.
246	3		_aProbabilistic Forecasting & Bayesian Data Assimilation
264		1	_aCambridge : _bCambridge University Press, _c2015.
300			_a1 online resource (x, 297 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
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://doi.org/10.1017/CBO9781107706804
907			_a.b16848846 _b2020-12-22 _c2020-12-22
942			_n0
998			_a1 _b2020-12-22 _cm _da _feng _genk _y0 _z.b16848846
999			_c652227 _d652227