000			03200nam a2200349 a 4500
005			20250930133612.0
008			120621s2011 enka b 001 0 eng
020			_a9780521196765 (hbk.) _cRM268.60
020			_a0521196760 (hbk.)
039		9	_a201305171602 _bzabidah _c201305131120 _drasyilla _y06-21-2012 _zrasyilla
040			_aDLC _cDLC _dUKM
090			_aQA280.B397
090			_aQA280 _b.B397
245	0	0	_aBayesian time series models / _cedited by David Barber, A. Taylan Cemgil, Silvia Chiappa.
260			_aCambridge, UK : _bCambridge University Press, _c2011.
300			_axiii, 417 p. : _bill. ; _c26 cm.
504			_aIncludes bibliographical references and index.
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'-- _cProvided by publisher.
520			_a'Time series appear in a variety of disciplines, from finance to physics, computer science to biology. The origins of the subject and diverse applications in the engineering and physics literature at times obscure the commonalities in the underlying models and techniques. A central aim of this book is an attempt to make modern time series techniques accessible to a broad range of researchers, based on the unifying concept of probabilistic models. These techniques facilitate access to the modern time series literature, including financial time series prediction, video-tracking, music analysis, control and genetic sequence analysis. A particular feature of the book is that it brings together leading researchers that span the more traditional disciplines of statistics, control theory, engineering and signal processing,to the more recent area machine learning and pattern recognition'-- _cProvided by publisher.
650		0	_aTime-series analysis. _961130
650		0	_aBayesian statistical decision theory.
700	1		_aBarber, David, _d1963-
700	1		_aCemgil, Ali Taylan.
700	1		_aChiappa, Silvia.
907			_a.b15409685 _b2019-11-12 _c2019-11-12
942			_c01 _n0 _kQA280.B397
914			_avtls003505920
990			_aza
991			_aFakulti Sains Sosial dan Kemanusiaan
998			_at _b2012-08-06 _cm _da _feng _genk _y0 _z.b15409685
999			_c524408 _d524408