000			03776nam a22004815i 4500
001			vtls000562112
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007			cr nn 008mamaa
008			170213s2015 ja | s |||| 0|eng d
020			_a9784431553397 _9978-4-431-55339-7
024	7		_a10.1007/978-4-431-55339-7 _2doi
035			_ato000562112
040			_aSpringer _cSpringer _dRU-ToGU
050		4	_aQA276-280
072		7	_aPBT _2bicssc
072		7	_aMAT029000 _2bisacsh
082	0	4	_a519.5 _223
245	1	0	_aModern Methodology and Applications in Spatial-Temporal Modeling _helectronic resource _cedited by Gareth William Peters, Tomoko Matsui.
250			_a1st ed. 2015.
260			_aTokyo : _bSpringer Japan : _bImprint: Springer, _c2015.
300			_aXV, 111 p. 17 illus., 4 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aSpringerBriefs in Statistics, _x2191-544X
505	0		_a1 Nonparametric Bayesian Inference with Kernel Mean Embedding (Kenji Fukumizu) -- 2 How to Utilise Sensor Network Data to Efficiently Perform Model Calibration and Spatial Field Reconstruction (Gareth W. Peters, Ido Nevat and Tomoko Matsui) -- 3 Speech and Music Emotion Recognition using Gaussian Processes (Konstantin Markov and Tomoko Matsui) -- 4 Topic Modeling for Speech and Language Processing (Jen-Tzung Chien).
520			_aThis book provides a modern introductory tutorial on specialized methodological and applied aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter deals with non-parametric Bayesian inference via a recently developed framework known as kernel mean embedding which has had a significant influence in machine learning disciplines. The second chapter takes up non-parametric statistical methods for spatial field reconstruction and exceedance probability estimation based on Gaussian process-based models in the context of wireless sensor network data. The third chapter presents signal-processing methods applied to acoustic mood analysis based on music signal analysis. The fourth chapter covers models that are applicable to time series modeling in the domain of speech and language processing. This includes aspects of factor analysis, independent component analysis in an unsupervised learning setting. The chapter moves on to include more advanced topics on generalized latent variable topic models based on hierarchical Dirichlet processes which recently have been developed in non-parametric Bayesian literature. The final chapter discusses aspects of dependence modeling, primarily focusing on the role of extreme tail-dependence modeling, copulas, and their role in wireless communications system models.
650		0	_aStatistics. _9124796
650	1	4	_aStatistics. _9124796
650	2	4	_aStatistical Theory and Methods. _9303276
650	2	4	_aStatistics and Computing/Statistics Programs. _9303277
650	2	4	_aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. _9410653
700	1		_aPeters, Gareth William. _eeditor. _9469501
700	1		_aMatsui, Tomoko. _eeditor. _9469502
710	2		_aSpringerLink (Online service) _9143950
773	0		_tSpringer eBooks
830		0	_aSpringerBriefs in Statistics, _9446803
856	4	0	_uhttp://dx.doi.org/10.1007/978-4-431-55339-7
912			_aZDB-2-SMA
999			_c416492