000			03935nam a22003858i 4500
001			CR9781139941433
005			20250919142038.0
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007			cr||||||||||||
008			140304s2015||||enk o ||1 0|eng|d
020			_a9781139941433 (ebook)
020			_z9781107079199 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	4	_aRC346 _b.A38 2015
082	0	0	_a616.8001/1 _223
245	0	0	_aAdvanced state space methods for neural and clinical data / _cedited by Zhe Chen.
246	3		_aAdvanced State Space Methods for Neural & Clinical Data
264		1	_aCambridge : _bCambridge University Press, _c2015.
300			_a1 online resource (xxii, 374 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		_aInference and learning in latent Markov models / D. Barber and S. Chiappa -- State space methods for MEG source reconstruction / M. Fukushima, O. Yamashita, and M. Sato -- Autoregressive modeling of FMRI time series : state space approaches and the general linear model / A. Galka [and others] -- State space models and their spectral decomposition in dynamic causal modeling / R. Moran -- Estimating state and parameters in state space models of spike trains / J.H. Macke, L. Buesing, and M. Sahani -- Bayesian inference for latent stepping and ramping models of spike train data / K.W. Latimer, A.C. Huk, and J.W. Pillow -- Probabilistic approaches to uncover rat hippocampal population codes / Z. Chen, F. Kloosterman, and M.A. Wilson -- Neural decoding in motor cortex using state space models with hidden states / W. Wuand S. Liu -- State-space modeling for analysis of behavior in learning experiments / A.C. Smith -- Bayesian nonparametric learning of switching dynamics in cohort physiological time series : application in critical care patient monitoring / L.H. Lehman, M.J. Johnson, S. Nemati, R.P. Adams and R.G. Mark -- Identifying outcome-discriminative dynamics in multivariate physiological cohort time series / S. Nemati and R.P. Adams -- A dynamic point process framework for assessing heartbeat dynamics and cardiovascular functions / Z. Chen and R. Barbieri -- Real-time segmentation and tracking of brain metabolic state in ICU EEG recordings of burst suppression / M.B. Westover, S. Ching, M.M. Shafi, S.S. Cash and E.N. Brown -- Signal quality indices for state-space electrophysiological signal processing and vice versa / J. Oster and G.D. Clifford.
520			_aThis authoritative work provides an in-depth treatment of state space methods, with a range of applications in neural and clinical data. Advanced and state-of-the-art research topics are detailed, including topics in state space analyses, maximum likelihood methods, variational Bayes, sequential Monte Carlo, Markov chain Monte Carlo, nonparametric Bayesian, and deep learning methods. Details are provided on practical applications in neural and clinical data, whether this is characterising time series data from neural spike trains recorded from the rat hippocampus, the primate motor cortex, or the human EEG, MEG or fMRI, or physiological measurements of heartbeats or blood pressures. With real-world case studies of neuroscience experiments and clinical data sets, and written by expert authors from across the field, this is an ideal resource for anyone working in neuroscience and physiological data analysis.
650		0	_aNervous system _xDiseases.
650		0	_aState-space methods.
700	1		_aChen, Zhe, _d1976- _eeditor.
776	0	8	_iPrint version: _z9781107079199
856	4	0	_uhttps://doi.org/10.1017/CBO9781139941433
907			_a.b16843484 _b2020-12-22 _c2020-12-22
942			_n0
998			_a1 _b2020-12-22 _cm _da _feng _genk _y0 _z.b16843484
999			_c651692 _d651692