000			04625nam a22003618i 4500
001			CR9780511984679
003			UkCbUP
005			20200124160244.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			101018s2011||||enk o ||1 0|eng|d
020			_a9780511984679 (ebook)
020			_z9780521196765 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
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 (xiii, 417 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).
505	0	0	_g1. _tInference and estimation in probabilistic time series models / _rDavid Barber, A. Taylan Cemgil and Silvia Chiappa -- _gI. _tMonte Carlo: _g2. _tAdaptive Markov chain Monte Carlo: theory and methods / _rYves Atchadé, Gersende Fort, Eric Moulines and Pierre Priouret; _g3. _tAuxiliary particle filtering: recent developments / _rNick Whiteley and Adam M. Johansen; _g4. _tMonte Carlo probabilistic inference for diffusion processes: a methodological framework / _rOmiros Papaspiliopoulos -- _gII. _tDeterministic Approximations: _g5. _tTwo problems with variational expectation maximisation for time series models / _rRichard Eric Turner and Maneesh Sahani; _g6. _tApproximate inference for continuous-time Markov processes / _rCédric Archambeau and Manfred Opper; _g7. _tExpectation propagation and generalised EP methods for inference in switching linear dynamical systems / _rOnno Zoeter and Tom Heskes; _g8. _tApproximate inference in switching linear dynamical systems using Gaussian mixtures / _rDavid Barber -- _gIII. _tSwitch Models: _g9. _tPhysiological monitoring with factorial switching linear dynamical systems / _rJohn A. Quinn and Christopher K.I. Williams; _g10. _tAnalysis of changepoint models / _rIdris A. Eckley, Paul Fearnhead and Rebecca Killick -- _gIV. _tMulti-Object Models: _g11. _tApproximate likelihood estimation of static parameters in multi-target models / _rSumeetpal S. Singh, Nick Whiteley and Simon J. Godsill; _g12. _tSequential inference for dynamically evolving groups of objects / _rSze Kim Pang, Simon J. Godsill, Jack Li, François Septier and Simon Hill; _g13. _tNon-commutative harmonic analysis in multi-object tracking / _rRisi Kondor -- _gV. _tNonparametric Models: _g14. Markov chain Monte Carlo algorithms for Gaussian processes / _rMichalis K. Titsias, Magnus Rattray and Neil D. Lawrence; _g15. _tNonparametric hidden Markov models / _rJurgen Van Gael and Zoubin Ghahramani; _g16. _tBayesian Gaussian process models for multi-sensor time series prediction / _rMichael A. Osborne, Alex Rogers, Stephen J. Roberts, Sarvapali D. Ramchurn and Nick R. Jennings -- _gVI. _tAgent-Based Models: _g17. Optimal control theory and the linear Bellman equation / _rHilbert J. Kappen; _g18. _tExpectation maximisation methods for solving (PO)MDPs and optimal control problems / _rMarc Toussaint, Amos Storkey and Stefan Harmeling.
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, _d1968- _eeditor.
700	1		_aCemgil, Ali Taylan, _eeditor.
700	1		_aChiappa, Silvia, _eeditor.
776	0	8	_iPrint version: _z9780521196765
856	4	0	_uhttps://doi.org/10.1017/CBO9780511984679
999			_c518781 _d518779