000			02289nam a22003377a 4500
001			sulb-eb0016378
003			BD-SySUS
005			20160405135335.0
008			101018s2011||||enk o ||1 0|eng|d
020			_a9780511984679 (ebook)
020			_z9780521196765 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP _dBD-SySUS
050	0	0	_aQA280 _b.B39 2011
082	0	0	_a519.5/5 _222
245	0	0	_aBayesian Time Series Models / _cedited by David Barber, A. Taylan Cemgil, Silvia Chiappa.
264		1	_aCambridge : _bCambridge University Press, _c2011.
300			_a1 online resource (432 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 04 Apr 2016).
520			_a'What's going to happen next?' Time series data hold the answers, and Bayesian methods represent the cutting edge in learning what they have to say. This ambitious book is the first unified treatment of the emerging knowledge-base in Bayesian time series techniques. Exploiting the unifying framework of probabilistic graphical models, the book covers approximation schemes, both Monte Carlo and deterministic, and introduces switching, multi-object, non-parametric and agent-based models in a variety of application environments. It demonstrates that the basic framework supports the rapid creation of models tailored to specific applications and gives insight into the computational complexity of their implementation. The authors span traditional disciplines such as statistics and engineering and the more recently established areas of machine learning and pattern recognition. Readers with a basic understanding of applied probability, but no experience with time series analysis, are guided from fundamental concepts to the state-of-the-art in research and practice.
650		0	_aTime-series analysis
650		0	_aBayesian statistical decision theory
700	1		_aBarber, David, _eeditor.
700	1		_aCemgil, A. Taylan, _eeditor.
700	1		_aChiappa, Silvia, _eeditor.
776	0	8	_iPrint version: _z9780521196765
856	4	0	_uhttp://dx.doi.org/10.1017/CBO9780511984679
942			_2Dewey Decimal Classification _ceBooks
999			_c37816 _d37816